{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Direction</th>\n",
       "      <th>District</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Floor</th>\n",
       "      <th>Garden</th>\n",
       "      <th>Id</th>\n",
       "      <th>Layout</th>\n",
       "      <th>Price</th>\n",
       "      <th>Region</th>\n",
       "      <th>Renovation</th>\n",
       "      <th>Size</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东西</td>\n",
       "      <td>灯市口</td>\n",
       "      <td>未知</td>\n",
       "      <td>6</td>\n",
       "      <td>锡拉胡同21号院</td>\n",
       "      <td>101102647043</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>780.0</td>\n",
       "      <td>东城</td>\n",
       "      <td>精装</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>南北</td>\n",
       "      <td>东单</td>\n",
       "      <td>无电梯</td>\n",
       "      <td>6</td>\n",
       "      <td>东华门大街</td>\n",
       "      <td>101102650978</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>705.0</td>\n",
       "      <td>东城</td>\n",
       "      <td>精装</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>南西</td>\n",
       "      <td>崇文门</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>16</td>\n",
       "      <td>新世界中心</td>\n",
       "      <td>101102672743</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>东城</td>\n",
       "      <td>其他</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>南</td>\n",
       "      <td>崇文门</td>\n",
       "      <td>未知</td>\n",
       "      <td>7</td>\n",
       "      <td>兴隆都市馨园</td>\n",
       "      <td>101102577410</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>420.0</td>\n",
       "      <td>东城</td>\n",
       "      <td>精装</td>\n",
       "      <td>39.0</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>南</td>\n",
       "      <td>陶然亭</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>19</td>\n",
       "      <td>中海紫御公馆</td>\n",
       "      <td>101102574696</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>998.0</td>\n",
       "      <td>东城</td>\n",
       "      <td>精装</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Direction District Elevator  Floor    Garden            Id Layout   Price  \\\n",
       "0        东西      灯市口       未知      6  锡拉胡同21号院  101102647043   3室1厅   780.0   \n",
       "1        南北       东单      无电梯      6     东华门大街  101102650978   2室1厅   705.0   \n",
       "2        南西      崇文门      有电梯     16     新世界中心  101102672743   3室1厅  1400.0   \n",
       "3         南      崇文门       未知      7    兴隆都市馨园  101102577410   1室1厅   420.0   \n",
       "4         南      陶然亭      有电梯     19    中海紫御公馆  101102574696   2室2厅   998.0   \n",
       "\n",
       "  Region Renovation   Size  Year  \n",
       "0     东城         精装   75.0  1988  \n",
       "1     东城         精装   60.0  1988  \n",
       "2     东城         其他  210.0  1996  \n",
       "3     东城         精装   39.0  2004  \n",
       "4     东城         精装   90.0  2010  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "f = open('清洗后的数据.csv')\n",
    "file = pd.read_csv(f, encoding = 'utf-8')\n",
    "file.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Direction</th>\n",
       "      <th>District</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Floor</th>\n",
       "      <th>Garden</th>\n",
       "      <th>Id</th>\n",
       "      <th>Layout</th>\n",
       "      <th>Price</th>\n",
       "      <th>Region</th>\n",
       "      <th>Renovation</th>\n",
       "      <th>Size</th>\n",
       "      <th>Year</th>\n",
       "      <th>avgPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东西</td>\n",
       "      <td>灯市口</td>\n",
       "      <td>未知</td>\n",
       "      <td>6</td>\n",
       "      <td>锡拉胡同21号院</td>\n",
       "      <td>101102647043</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>780.0</td>\n",
       "      <td>东城</td>\n",
       "      <td>精装</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1988</td>\n",
       "      <td>104000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>南北</td>\n",
       "      <td>东单</td>\n",
       "      <td>无电梯</td>\n",
       "      <td>6</td>\n",
       "      <td>东华门大街</td>\n",
       "      <td>101102650978</td>\n",
       "      <td>2室1厅</td>\n",
       "      <td>705.0</td>\n",
       "      <td>东城</td>\n",
       "      <td>精装</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1988</td>\n",
       "      <td>117500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>南西</td>\n",
       "      <td>崇文门</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>16</td>\n",
       "      <td>新世界中心</td>\n",
       "      <td>101102672743</td>\n",
       "      <td>3室1厅</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>东城</td>\n",
       "      <td>其他</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>66666.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>南</td>\n",
       "      <td>崇文门</td>\n",
       "      <td>未知</td>\n",
       "      <td>7</td>\n",
       "      <td>兴隆都市馨园</td>\n",
       "      <td>101102577410</td>\n",
       "      <td>1室1厅</td>\n",
       "      <td>420.0</td>\n",
       "      <td>东城</td>\n",
       "      <td>精装</td>\n",
       "      <td>39.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>107692.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>南</td>\n",
       "      <td>陶然亭</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>19</td>\n",
       "      <td>中海紫御公馆</td>\n",
       "      <td>101102574696</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>998.0</td>\n",
       "      <td>东城</td>\n",
       "      <td>精装</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>110888.888889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Direction District Elevator  Floor    Garden            Id Layout   Price  \\\n",
       "0        东西      灯市口       未知      6  锡拉胡同21号院  101102647043   3室1厅   780.0   \n",
       "1        南北       东单      无电梯      6     东华门大街  101102650978   2室1厅   705.0   \n",
       "2        南西      崇文门      有电梯     16     新世界中心  101102672743   3室1厅  1400.0   \n",
       "3         南      崇文门       未知      7    兴隆都市馨园  101102577410   1室1厅   420.0   \n",
       "4         南      陶然亭      有电梯     19    中海紫御公馆  101102574696   2室2厅   998.0   \n",
       "\n",
       "  Region Renovation   Size  Year       avgPrice  \n",
       "0     东城         精装   75.0  1988  104000.000000  \n",
       "1     东城         精装   60.0  1988  117500.000000  \n",
       "2     东城         其他  210.0  1996   66666.666667  \n",
       "3     东城         精装   39.0  2004  107692.307692  \n",
       "4     东城         精装   90.0  2010  110888.888889  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 构建单价变量，即Price / Size\n",
    "file['avgPrice'] = ( file['Price'] / file['Size'] ) * 10000\n",
    "file.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>1420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>丰台</td>\n",
       "      <td>2717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>亦庄开发区</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大兴</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>密云</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>平谷</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>怀柔</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>房山</td>\n",
       "      <td>1373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>昌平</td>\n",
       "      <td>2653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>朝阳</td>\n",
       "      <td>2711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>海淀</td>\n",
       "      <td>2758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>石景山</td>\n",
       "      <td>833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>西城</td>\n",
       "      <td>1983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>通州</td>\n",
       "      <td>1539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>门头沟</td>\n",
       "      <td>477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>顺义</td>\n",
       "      <td>1157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region  Price\n",
       "0      东城   1420\n",
       "1      丰台   2717\n",
       "2   亦庄开发区    460\n",
       "3      大兴   2005\n",
       "4      密云     12\n",
       "5      平谷     38\n",
       "6      怀柔     14\n",
       "7      房山   1373\n",
       "8      昌平   2653\n",
       "9      朝阳   2711\n",
       "10     海淀   2758\n",
       "11    石景山    833\n",
       "12     西城   1983\n",
       "13     通州   1539\n",
       "14    门头沟    477\n",
       "15     顺义   1157"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_cnt = file[['Price', 'Region']].groupby('Region').count()\n",
    "region_cnt = region_cnt.reset_index(drop = False)\n",
    "region_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>1178187.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>丰台</td>\n",
       "      <td>1402006.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>亦庄开发区</td>\n",
       "      <td>251044.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大兴</td>\n",
       "      <td>901359.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>密云</td>\n",
       "      <td>5104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>平谷</td>\n",
       "      <td>11845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>怀柔</td>\n",
       "      <td>10628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>房山</td>\n",
       "      <td>488639.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>昌平</td>\n",
       "      <td>1223921.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>朝阳</td>\n",
       "      <td>1991173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>海淀</td>\n",
       "      <td>2248436.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>石景山</td>\n",
       "      <td>385929.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>西城</td>\n",
       "      <td>1606128.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>通州</td>\n",
       "      <td>695450.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>门头沟</td>\n",
       "      <td>185480.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>顺义</td>\n",
       "      <td>602478.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region      Price\n",
       "0      东城  1178187.8\n",
       "1      丰台  1402006.4\n",
       "2   亦庄开发区   251044.0\n",
       "3      大兴   901359.2\n",
       "4      密云     5104.0\n",
       "5      平谷    11845.0\n",
       "6      怀柔    10628.0\n",
       "7      房山   488639.3\n",
       "8      昌平  1223921.5\n",
       "9      朝阳  1991173.0\n",
       "10     海淀  2248436.0\n",
       "11    石景山   385929.4\n",
       "12     西城  1606128.5\n",
       "13     通州   695450.3\n",
       "14    门头沟   185480.8\n",
       "15     顺义   602478.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_sum = file[['Price', 'Region']].groupby('Region').sum()\n",
    "region_sum = region_sum.reset_index(drop = False)\n",
    "region_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Price_x</th>\n",
       "      <th>Price_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>1420</td>\n",
       "      <td>1178187.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>丰台</td>\n",
       "      <td>2717</td>\n",
       "      <td>1402006.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>亦庄开发区</td>\n",
       "      <td>460</td>\n",
       "      <td>251044.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大兴</td>\n",
       "      <td>2005</td>\n",
       "      <td>901359.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>密云</td>\n",
       "      <td>12</td>\n",
       "      <td>5104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>平谷</td>\n",
       "      <td>38</td>\n",
       "      <td>11845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>怀柔</td>\n",
       "      <td>14</td>\n",
       "      <td>10628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>房山</td>\n",
       "      <td>1373</td>\n",
       "      <td>488639.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>昌平</td>\n",
       "      <td>2653</td>\n",
       "      <td>1223921.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>朝阳</td>\n",
       "      <td>2711</td>\n",
       "      <td>1991173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>海淀</td>\n",
       "      <td>2758</td>\n",
       "      <td>2248436.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>石景山</td>\n",
       "      <td>833</td>\n",
       "      <td>385929.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>西城</td>\n",
       "      <td>1983</td>\n",
       "      <td>1606128.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>通州</td>\n",
       "      <td>1539</td>\n",
       "      <td>695450.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>门头沟</td>\n",
       "      <td>477</td>\n",
       "      <td>185480.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>顺义</td>\n",
       "      <td>1157</td>\n",
       "      <td>602478.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region  Price_x    Price_y\n",
       "0      东城     1420  1178187.8\n",
       "1      丰台     2717  1402006.4\n",
       "2   亦庄开发区      460   251044.0\n",
       "3      大兴     2005   901359.2\n",
       "4      密云       12     5104.0\n",
       "5      平谷       38    11845.0\n",
       "6      怀柔       14    10628.0\n",
       "7      房山     1373   488639.3\n",
       "8      昌平     2653  1223921.5\n",
       "9      朝阳     2711  1991173.0\n",
       "10     海淀     2758  2248436.0\n",
       "11    石景山      833   385929.4\n",
       "12     西城     1983  1606128.5\n",
       "13     通州     1539   695450.3\n",
       "14    门头沟      477   185480.8\n",
       "15     顺义     1157   602478.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region = pd.merge(region_cnt, region_sum, how = 'left', on = 'Region')\n",
    "region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Price_x</th>\n",
       "      <th>Price_y</th>\n",
       "      <th>avgSumPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>1420</td>\n",
       "      <td>1178187.8</td>\n",
       "      <td>829.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>丰台</td>\n",
       "      <td>2717</td>\n",
       "      <td>1402006.4</td>\n",
       "      <td>516.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>亦庄开发区</td>\n",
       "      <td>460</td>\n",
       "      <td>251044.0</td>\n",
       "      <td>545.748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大兴</td>\n",
       "      <td>2005</td>\n",
       "      <td>901359.2</td>\n",
       "      <td>449.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>密云</td>\n",
       "      <td>12</td>\n",
       "      <td>5104.0</td>\n",
       "      <td>425.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>平谷</td>\n",
       "      <td>38</td>\n",
       "      <td>11845.0</td>\n",
       "      <td>311.711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>怀柔</td>\n",
       "      <td>14</td>\n",
       "      <td>10628.0</td>\n",
       "      <td>759.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>房山</td>\n",
       "      <td>1373</td>\n",
       "      <td>488639.3</td>\n",
       "      <td>355.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>昌平</td>\n",
       "      <td>2653</td>\n",
       "      <td>1223921.5</td>\n",
       "      <td>461.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>朝阳</td>\n",
       "      <td>2711</td>\n",
       "      <td>1991173.0</td>\n",
       "      <td>734.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>海淀</td>\n",
       "      <td>2758</td>\n",
       "      <td>2248436.0</td>\n",
       "      <td>815.241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>石景山</td>\n",
       "      <td>833</td>\n",
       "      <td>385929.4</td>\n",
       "      <td>463.301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>西城</td>\n",
       "      <td>1983</td>\n",
       "      <td>1606128.5</td>\n",
       "      <td>809.949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>通州</td>\n",
       "      <td>1539</td>\n",
       "      <td>695450.3</td>\n",
       "      <td>451.885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>门头沟</td>\n",
       "      <td>477</td>\n",
       "      <td>185480.8</td>\n",
       "      <td>388.849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>顺义</td>\n",
       "      <td>1157</td>\n",
       "      <td>602478.0</td>\n",
       "      <td>520.724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region  Price_x    Price_y  avgSumPrice\n",
       "0      东城     1420  1178187.8      829.710\n",
       "1      丰台     2717  1402006.4      516.013\n",
       "2   亦庄开发区      460   251044.0      545.748\n",
       "3      大兴     2005   901359.2      449.556\n",
       "4      密云       12     5104.0      425.333\n",
       "5      平谷       38    11845.0      311.711\n",
       "6      怀柔       14    10628.0      759.143\n",
       "7      房山     1373   488639.3      355.892\n",
       "8      昌平     2653  1223921.5      461.335\n",
       "9      朝阳     2711  1991173.0      734.479\n",
       "10     海淀     2758  2248436.0      815.241\n",
       "11    石景山      833   385929.4      463.301\n",
       "12     西城     1983  1606128.5      809.949\n",
       "13     通州     1539   695450.3      451.885\n",
       "14    门头沟      477   185480.8      388.849\n",
       "15     顺义     1157   602478.0      520.724"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region['avgSumPrice'] = round(region['Price_y'] / region['Price_x'], 3)\n",
    "region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['东城', '丰台', '亦庄开发区', '大兴', '密云', '平谷', '怀柔', '房山', '昌平', '朝阳', '海淀', '石景山', '西城', '通州', '门头沟', '顺义']\n",
      "[829.71, 516.013, 545.748, 449.556, 425.333, 311.711, 759.143, 355.892, 461.335, 734.479, 815.241, 463.301, 809.949, 451.885, 388.849, 520.724]\n"
     ]
    }
   ],
   "source": [
    "xlist = list(region['Region'])\n",
    "ylist = list(region['avgSumPrice'])\n",
    "print(xlist)\n",
    "print(ylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']='SimHei'#设置中文显示\n",
    "plt.figure(figsize=(16,12))\n",
    "plt.bar(xlist, ylist)\n",
    "for a, b in zip(xlist, ylist):\n",
    "    plt.text(a, b + 0.02, b, ha = 'center', va = 'bottom', fontsize = 12)\n",
    "plt.title('各区总价统计情况')#绘制标题\n",
    "plt.savefig('./3.1各区总价统计情况柱状图.jpg')#保存图片\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>avgPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>1.397182e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>丰台</td>\n",
       "      <td>1.562359e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>亦庄开发区</td>\n",
       "      <td>2.157918e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大兴</td>\n",
       "      <td>9.024164e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>密云</td>\n",
       "      <td>2.871174e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>平谷</td>\n",
       "      <td>1.050431e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>怀柔</td>\n",
       "      <td>5.527094e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>房山</td>\n",
       "      <td>4.831562e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>昌平</td>\n",
       "      <td>1.137987e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>朝阳</td>\n",
       "      <td>1.897013e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>海淀</td>\n",
       "      <td>2.379424e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>石景山</td>\n",
       "      <td>4.623598e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>西城</td>\n",
       "      <td>2.118925e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>通州</td>\n",
       "      <td>6.904285e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>门头沟</td>\n",
       "      <td>1.919641e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>顺义</td>\n",
       "      <td>4.844664e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region      avgPrice\n",
       "0      东城  1.397182e+08\n",
       "1      丰台  1.562359e+08\n",
       "2   亦庄开发区  2.157918e+07\n",
       "3      大兴  9.024164e+07\n",
       "4      密云  2.871174e+05\n",
       "5      平谷  1.050431e+06\n",
       "6      怀柔  5.527094e+05\n",
       "7      房山  4.831562e+07\n",
       "8      昌平  1.137987e+08\n",
       "9      朝阳  1.897013e+08\n",
       "10     海淀  2.379424e+08\n",
       "11    石景山  4.623598e+07\n",
       "12     西城  2.118925e+08\n",
       "13     通州  6.904285e+07\n",
       "14    门头沟  1.919641e+07\n",
       "15     顺义  4.844664e+07"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_per = file[['avgPrice', 'Region']].groupby('Region').sum()\n",
    "region_per = region_per.reset_index(drop = False)\n",
    "region_per"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Price</th>\n",
       "      <th>avgPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>1420</td>\n",
       "      <td>1.397182e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>丰台</td>\n",
       "      <td>2717</td>\n",
       "      <td>1.562359e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>亦庄开发区</td>\n",
       "      <td>460</td>\n",
       "      <td>2.157918e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大兴</td>\n",
       "      <td>2005</td>\n",
       "      <td>9.024164e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>密云</td>\n",
       "      <td>12</td>\n",
       "      <td>2.871174e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>平谷</td>\n",
       "      <td>38</td>\n",
       "      <td>1.050431e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>怀柔</td>\n",
       "      <td>14</td>\n",
       "      <td>5.527094e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>房山</td>\n",
       "      <td>1373</td>\n",
       "      <td>4.831562e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>昌平</td>\n",
       "      <td>2653</td>\n",
       "      <td>1.137987e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>朝阳</td>\n",
       "      <td>2711</td>\n",
       "      <td>1.897013e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>海淀</td>\n",
       "      <td>2758</td>\n",
       "      <td>2.379424e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>石景山</td>\n",
       "      <td>833</td>\n",
       "      <td>4.623598e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>西城</td>\n",
       "      <td>1983</td>\n",
       "      <td>2.118925e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>通州</td>\n",
       "      <td>1539</td>\n",
       "      <td>6.904285e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>门头沟</td>\n",
       "      <td>477</td>\n",
       "      <td>1.919641e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>顺义</td>\n",
       "      <td>1157</td>\n",
       "      <td>4.844664e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region  Price      avgPrice\n",
       "0      东城   1420  1.397182e+08\n",
       "1      丰台   2717  1.562359e+08\n",
       "2   亦庄开发区    460  2.157918e+07\n",
       "3      大兴   2005  9.024164e+07\n",
       "4      密云     12  2.871174e+05\n",
       "5      平谷     38  1.050431e+06\n",
       "6      怀柔     14  5.527094e+05\n",
       "7      房山   1373  4.831562e+07\n",
       "8      昌平   2653  1.137987e+08\n",
       "9      朝阳   2711  1.897013e+08\n",
       "10     海淀   2758  2.379424e+08\n",
       "11    石景山    833  4.623598e+07\n",
       "12     西城   1983  2.118925e+08\n",
       "13     通州   1539  6.904285e+07\n",
       "14    门头沟    477  1.919641e+07\n",
       "15     顺义   1157  4.844664e+07"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region2 = pd.merge(region_cnt, region_per, how = 'left', on = 'Region')\n",
    "region2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Price</th>\n",
       "      <th>avgPrice</th>\n",
       "      <th>avgPerPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>东城</td>\n",
       "      <td>1420</td>\n",
       "      <td>1.397182e+08</td>\n",
       "      <td>98393.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>丰台</td>\n",
       "      <td>2717</td>\n",
       "      <td>1.562359e+08</td>\n",
       "      <td>57503.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>亦庄开发区</td>\n",
       "      <td>460</td>\n",
       "      <td>2.157918e+07</td>\n",
       "      <td>46911.268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大兴</td>\n",
       "      <td>2005</td>\n",
       "      <td>9.024164e+07</td>\n",
       "      <td>45008.301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>密云</td>\n",
       "      <td>12</td>\n",
       "      <td>2.871174e+05</td>\n",
       "      <td>23926.449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>平谷</td>\n",
       "      <td>38</td>\n",
       "      <td>1.050431e+06</td>\n",
       "      <td>27642.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>怀柔</td>\n",
       "      <td>14</td>\n",
       "      <td>5.527094e+05</td>\n",
       "      <td>39479.243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>房山</td>\n",
       "      <td>1373</td>\n",
       "      <td>4.831562e+07</td>\n",
       "      <td>35189.818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>昌平</td>\n",
       "      <td>2653</td>\n",
       "      <td>1.137987e+08</td>\n",
       "      <td>42894.359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>朝阳</td>\n",
       "      <td>2711</td>\n",
       "      <td>1.897013e+08</td>\n",
       "      <td>69974.651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>海淀</td>\n",
       "      <td>2758</td>\n",
       "      <td>2.379424e+08</td>\n",
       "      <td>86273.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>石景山</td>\n",
       "      <td>833</td>\n",
       "      <td>4.623598e+07</td>\n",
       "      <td>55505.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>西城</td>\n",
       "      <td>1983</td>\n",
       "      <td>2.118925e+08</td>\n",
       "      <td>106854.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>通州</td>\n",
       "      <td>1539</td>\n",
       "      <td>6.904285e+07</td>\n",
       "      <td>44862.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>门头沟</td>\n",
       "      <td>477</td>\n",
       "      <td>1.919641e+07</td>\n",
       "      <td>40244.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>顺义</td>\n",
       "      <td>1157</td>\n",
       "      <td>4.844664e+07</td>\n",
       "      <td>41872.636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region  Price      avgPrice  avgPerPrice\n",
       "0      东城   1420  1.397182e+08    98393.072\n",
       "1      丰台   2717  1.562359e+08    57503.082\n",
       "2   亦庄开发区    460  2.157918e+07    46911.268\n",
       "3      大兴   2005  9.024164e+07    45008.301\n",
       "4      密云     12  2.871174e+05    23926.449\n",
       "5      平谷     38  1.050431e+06    27642.930\n",
       "6      怀柔     14  5.527094e+05    39479.243\n",
       "7      房山   1373  4.831562e+07    35189.818\n",
       "8      昌平   2653  1.137987e+08    42894.359\n",
       "9      朝阳   2711  1.897013e+08    69974.651\n",
       "10     海淀   2758  2.379424e+08    86273.520\n",
       "11    石景山    833  4.623598e+07    55505.380\n",
       "12     西城   1983  2.118925e+08   106854.538\n",
       "13     通州   1539  6.904285e+07    44862.152\n",
       "14    门头沟    477  1.919641e+07    40244.046\n",
       "15     顺义   1157  4.844664e+07    41872.636"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region2['avgPerPrice'] = round(region2['avgPrice'] / region2['Price'], 3)\n",
    "region2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['东城', '丰台', '亦庄开发区', '大兴', '密云', '平谷', '怀柔', '房山', '昌平', '朝阳', '海淀', '石景山', '西城', '通州', '门头沟', '顺义']\n",
      "[98393.072, 57503.082, 46911.268, 45008.301, 23926.449, 27642.93, 39479.243, 35189.818, 42894.359, 69974.651, 86273.52, 55505.38, 106854.538, 44862.152, 40244.046, 41872.636]\n"
     ]
    }
   ],
   "source": [
    "xlist = list(region2['Region'])\n",
    "ylist = list(region2['avgPerPrice'])\n",
    "for i in range(0, len(ylist)):\n",
    "    ylist[i] = round(ylist[i], 3)\n",
    "print(xlist)\n",
    "print(ylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cOJF69ert9NyZmZksW7Ys9TwswG233cZ5553HbbfdxoknnsjkyZOpUqUKbdu2pV+/fjRt2pQaNWpw7733kpWVxVVXXcW8efNo27YtlStX5oorruDHP/7xPr1nUtKEGOOBHsN3kp+fH11iIUmSJEkHpxDCvBhj/q7aOTMrSZKkg16jG6Yc6CHs0tI7uhzoIUiJ4jOzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTE+VZhNoSQEUKYWfq5cgjh6RDCayGEi/ZFTZIkSZKkndllmA0h1AEeAWqUlq4G5sUYTwF6hBAO2Qe1771NmzbRv39/srOzycvLY+bMmQDccsstZGdnk52dzejRo1Pt58yZQ7du3ejUqRPPPffcTvvYUfud+e1vf8thhx3G6aefzmeffQZAt27dyMzMTP1KS0tjxowZbNq0iZ49e1K1alUaNWrEiy++uDdvjSRJkiTtc5W+RZvNQC9gUul2e+CG0s8zgPx9UHu57ABCCJcBlwE0aNDgWwx533vooYdYv349S5cu5e2336Zr165MmzaN6dOns2zZMlauXEmLFi3o3LkzVatW5fzzz2fkyJGEEOjZsyfvvfceY8eO3a6PhQsXsmXLlnLb16lTp9yxTJkyhSeffJJFixYxZcoUhgwZwgMPPMCkSZNSbT777DPatGlDq1atuOuuu6hcuTJFRUVMnjyZK664gvfff39/3TpJkiRJ2mO7nJmNMX4eY1xbplQDWFH6+TMgYx/UvjmGh2KM+THG/Hr16n27K9vHxo0bxxVXXEGlSpXIz8+nbt26TJ06lfz8fCpWrEhOTg4NGzakqKiIVatWceutt9KhQwfat29PTk4Oy5cvL7eP9957b4ftd2TixIkMGDCA9PR0fvrTn24zw7vVvffey5VXXknVqlU5/vjjeeCBB6hduzb/9V//tdO+JUmSJOn76NvMzH7TF0A1YC1Qs3R7b9cSYfXq1QCsW7eOZcuW0blzZ3r06MEvfvELFi1axOrVq2natClpaWkcc8wxbN68maeeeoqvvvqK3NzccvuoW7cujRo12mH78ixfvpxLLrkEgBAClStXZv369dSoUbIyfNOmTYwdO5bCwkIAOnfunDr2xRdfpHXr1nv/5kiSJEnSPrQ7bzOeB5xa+vl4YOk+qH3vnX/++QwYMIBhw4bRsWNHjjnmGHJzc8nJyaFLly5cdNFFDBo0iLS0tNQx9913H3379qV///5UrFix3D4aNWq0w/Y7snnzZtLT01Pb1atXZ+3a/0ymP/HEE/zkJz+hZs2a2xy3ceNGbrzxRq6//vq9cEckSZIkaf/ZnZnZR4BnQwg/BnKBNyhZJrw3a997l19+ORkZGbz22mu89dZbPPbYY/z5z3+mcePGTJs2jS1bttCpUydatWpFy5YtARg4cCA9e/bkpJNOolOnTuX2UdY32//oRz8qdyx16tRhzZo1qe0NGzZQocJ/fk7xl7/8hRtuuGG7437729/SvHlzzjzzzL1xSyRJkiRpv/nWM7Mxxval/10GdAReA86IMW7e27W9d3n71rnnnku7du049thj6d69O7Nnz04tB65QoQLNmzdn7ty5LFq0iL///e8AZGdn07JlS957771y+wB22r48+fn5vP766wAUFxezZMkSDj30UADWrl1LYWEhp5xyyjbHPPfcc/ztb39jxIgRe/GOSJIkSdL+sTvLjIkxrowxji/7Yqi9XUuK22+/ndtuu40QAo0bN2bcuHEUFhbyyiuv8Nhjj9GsWTNWrlxJr169WLNmDatWrWLOnDmccMIJ5fYB7LL9N/Xo0YP77ruPadOmcd1113HaaadRuXJlAP73f/+XNm3abLNMedGiRfTr14/HH3+c2rVr76M7I0mSJEn7zu4sM1apZ599lrS0NM466ywArrrqKubNm0fbtm2pXLkyV1xxBT/+8Y8B6NevH02bNqVGjRrce++9ZGVlldsHQNu2bctt/+GHH9KlS5fUrO1WRx55JA888AC//vWvqV+/PiNHjkztmz59+nYveBo+fDhr167l7LPPTtUKCgo4/PDD9+4NkiRJkqR9JMQYD/QYvpP8/Pw4d+7cAz0MSZIkJUijG6Yc6CHs0tI7uhzoIUjfCyGEeTHG/F21261lxpIkSZIkHUguM94H/MmfJEmSJO1bzsxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJkiQpcQyzkiRJkqTEMcxKkiRJkhLHMCtJkiRJShzDrCRJ0h665ZZbyM7OJjs7m9GjR6fqa9asoUGDBnzwwQep2pgxY6hXrx7Vq1fnuuuuA+DRRx8lMzMz9atWrVpcdNFFACxatIhevXpx+umn85e//GWXY2natGmqn+zs7FT9jjvuoFatWtSuXZu77rprb126JB0wlQ70ACRJkpLsnXfeYfr06SxbtoyVK1fSokULOnfuTEZGBkOGDKF3794cccQRAPzrX//ipptu4oUXXqBevXqcdNJJdO/enT59+tCnT59Un3369OGcc85h8+bNdO/enZtuuolGjRrRrVs3WrZsyTHHHFPuWIqLiwkhUFRUtE399ddf57HHHqOgoIAvv/yS1q1b07NnT3JycvbdjZGkfcwwK0mStAfeffdd8vPzqVixIjk5OTRs2JCioiJWrlzJxIkTWbBgQart119/zWOPPUbz5s0BOPHEE1m+fPk2/b3//vssXLiQs88+m48++ogBAwbQvXt3AFq1asX777+/wzBbWFhIXl7edvXq1avz6KOPpkJ1o0aNWLlypWFWUqK5zFiSJGkP5ObmMnnyZFatWsXMmTNZvXo1TZs25ZprriE9PZ2+ffsyYsQIYowcccQRnHTSSUDJLOrs2bNp1arVNv3df//9XH311QBkZGRw2WWXEWNk+vTpFBYW0rZt2x2OpaCggFdffZWsrCyOPPJIJk+eDMDxxx/PscceC8DKlSv58MMPadas2b64HZK03xhmJUmS9kBubi45OTl06dKFiy66iEGDBjF37lxmzZpF//796devH3fddRcjR47c5rg777yT0047jcaNG6dqGzZs4JlnnqFnz57btB0/fjxdu3bl3HPPpVatWjscS82aNRk2bBgrV65k7NixXHrppWzcuHGbNoMHD6Z///7UqFFjL1y9JB04LjOWJEnaA3/+859p3Lgx06ZNY8uWLXTq1Ini4mK6du3KtddeC8D69esZP348l19+OVCyHPjBBx9k3rx52/T19NNP0759e6pVq7ZNvVevXnTq1IlTTz2VLl260L59+3LH0q9fv9Tn1q1b07BhQwoLC1Ozv8899xyvvvoqb7/99t66fEk6YJyZlSRJ2gOzZ88mNzcXgAoVKtC8eXOqV69OgwYNUm2qVq1K7dq1Afj888/p1asXDzzwAFlZWdv09cQTT3DOOeektletWsXrr78OQO3atTnjjDP45z//ucOxjBkzhk2bNqW2V6xYQYUKJf/c+/DDD7nkkksYO3ass7KSDgqGWUmSpD3QuHFjxo0bR2FhIa+88gqPPfYYzZo1Y+rUqXzyySds2LCBUaNG0a5dOwB69+7NmWeeybnnnrtNP1u2bOHFF1/cZtb1yy+/pHv37ixfvpzPP/+cadOmbfeMbVkzZszgrrvuori4mOHDh5OWlkZeXh4bN27k7LPP5pe//OVOj5ekJDHMSpIk7YGrrrqK+vXr07ZtW3r06MGFF15Iu3btGDx4MKeccgrZ2dnUr1+fiy++mMLCQp599lnGjh2b+i7Ye++9FyhZenz44YeTnp6e6rtx48bcfvvttGnThry8PK688kpatmwJQGZm5nbPw952221MnTqVjIwMxo8fz+TJk6lSpQpTp07l7bff5o477kid94knnth/N0mS9oEQYzzQY/hO8vPz49y5cw/0MHaq0Q1TDvQQdmnpHV0O9BAkSZL2G/99JiVHCGFejDF/V+2cmZUkSZIkJY5vM5YkSdpLnP2TpP3HmVlJkiRJUuIYZiVJkiRJiWOYlSRJkiQljmFWkiRJkpQ4hllJkiRJUuIYZiVJkiRJiWOYlSRJkiQljmFWkiRJkpQ4hllJkiRJUuIYZiVJkiRJiWOYlSRJkiQljmFWkiRJkpQ4hllJkiRJUuIYZiVJkiRJiWOYlSRJkiQljmFWkiRJkpQ4hllJkiRJUuIYZiVJkiRJiWOYlSRJkiQljmFWkiRJkpQ4hllJkiRJUuIYZiVJkiRJiWOYlSRJkiQljmFWkiRJkpQ4hllJkiRJUuIYZiVJkiRJiWOYlSRJkiQljmFWkiRJkpQ4hllJkiRJUuIYZiVJkiRJiWOYlSRJkiQljmFWkiRJkpQ4hllJkiRJUuJ85zAbQqgTQng2hDA3hDCytPbHEMLrIYQhZdrtdk2SJEmSpJ3ZnZnZC4CxMcZ84JAQwq+AijHGNsARIYQmIYTuu1vbS9clSZIkSTqIVdqNYz4FmoUQagM5wFpgfOm+acCpwAl7UHv/mycMIVwGXAbQoEGD3RiyJEmSJOlgsjszs68CDYGfA+8CacCK0n2fARlAjT2obSfG+FCMMT/GmF+vXr3dGLIkSZIk6WCyO2H2RuDyGOMtwAKgD1CtdF/N0j6/2IOaJEmSJEk7tTvhsQ6QF0KoCJwE3EHJ8mCA44GlwLw9qEmSJEmStFO788zs7cBoSpYavw7cA8wMIWQBZwGtgbgHNUmSJEmSduo7z8zGGN+MMR4bY6wZY+wYY/wcaA/MBjrEGNfuSW1vXJQkSZIk6eC2OzOz24kx/pv/vJV4j2uSJEmSJO2ML1ySJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDixmyqeAAAgAElEQVSGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJO1Xa9asoUGDBnzwwQcADBkyhIYNG9KkSRMmTpyYalde/dFHHyUzMzP1q1atWlx00UXb9P/yyy/ToUOH3RoLQKdOncjIyEidY9WqVal906dP54wzztjta5ck7T2VDvQAJEnSD8uQIUPo3bs3RxxxBM888wyzZs1iwYIFFBUVccopp3DiiSfyzjvvlFvv06cPffr0SfXVp08fzjnnnNT2xo0b6d+/P1lZWd95LFstXLiQVatWUaHCtj/zf/PNN+nTpw9NmjTZwzsgSdobDLOSJGm/KSgoYOLEiSxYsACAcePGcfHFF1OtWjUaN25MixYtmDt3LpMmTSq33qhRo1Rf77//PgsXLuTss89O1YYOHUqLFi22mU39tmMBWLVqFYcddth2QRZgxIgRDB06lDFjxuzBHZAk7S0uM5YkSfvNNddcQ3p6On379mXEiBEArF69GoDNmzfz7rvvUrdu3Z3Wt7r//vu5+uqrU9vz589n0qRJ3Hjjjbs1lhgjBQUFLFmyhOzsbHJycnj44YdT7f/0pz9tM4MrSTqwnJmVJEn7xaxZs5g1axZ33nknOTk53HDDDQwcOJDf/OY3fPHFF8ycOZPi4mJOPfVUiouLueCCC7arb7VhwwaeeeYZhg0bBkCMkf79+zN8+HCqVq26W2MJIdCoUSMGDx7M1VdfzeLFizn55JPp2rUrmZmZhBD22b2RJH13hllJkrRfvPHGG3Tt2pVrr70WgPXr1zN+/HgmTJjA1KlTmTNnDr/+9a+pVKkSnTt3Lre+1dNPP0379u2pVq0aACNHjiQvL4+2bduydOnS3R7LlClTOPPMMwFo0qQJHTp0YMaMGfTs2XMv3w1J0p5ymbEkSdov0tPTadCgQWq7atWq1K5dmw4dOtC3b1+qVq3KFVdckdq/ozrAE088sc2LnyZPnsyECRPIzMykZcuWzJo1i5YtW37nsTz99NN89NFHqfqKFSvKfX5WknTg+aezJEnaL0499VSmTp3KJ598woYNGxg1ahTt2rUD4Pbbb2fIkCFUr159m2PKq2/ZsoUXX3yR9u3bp2rPPvssH3/8MUVFRcyZM4eTTz6ZOXPmfOexFBYWcvPNN7N+/XqeeuopFixYkBqjJOn7xWXGkiRpvzj66KMZPHgwp5xyCp9++ildu3bl4osv5p133mHevHk88sgj27TfUb2wsJDDDz+c9PT0b3XezMxMli1bRpUqVXY5lk2bNtG7d28yMjI4+uijmThxIvXq1dvzi5ck7XUhxnigx/Cd5Ofnx7lz5x7oYexUoxumHOgh7NLSO7oc6CFIknTQ8d8A31/+3kjJEUKYF2PM31U7lxlLkiRJkhLHZcaSJOmAcsZMkrQ7nJmVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDiGWUmSJElS4hhmJUmSJEmJY5iVJEmSJCWOYVaSJEmSlDi7HWZDCA+EELqWfv5jCOH1EMKQMvt3uyZJkiRJ0s7sVpgNIfwYyIwxPh1C6A5UjDG2AY4IITTZk9peui5JkiRJ0kHsO4fZEEJl4GFgaQihG9AeGF+6expw6h7WJEmSJEnaqd2Zme0HzAf+B2gFXAWsKN33GZAB1NiD2nZCCJeFEOaGEOZ+8sknuzFkSZIkSdLBZHfC7AnAQzHGIuCvwAygWum+mqV9frEHte3EGB+KMebHGPPr1au3G0OWJEmSJB1MdifMLgKOKP2cDzTiP8uDjweWAvP2oCZJkiRJ0k5V2o1j/gj8KYTwU6AyJc+9Tg4hZAFnAa2BCMzczZokSZIkSTv1nWdmY4zrYoznxRjbxhjbxBiXURJoZwMdYoxrY4yf725tb1yUdk/Tpk3JzMwkMzOT7Oxshg0bltrOzMykZs2a3HLLLeW23erBBx8kMzOT/Px8lixZkqo///zznHXWWXTt2pU333xzp+PYvHkzl156KfXq1eO8885j48aNAHz22We0b9+ezMxMTj75ZBYuXAjAv//9b8444wzS0tLIzc2lsLBwb98aSZIkSd8zu/09s2XFGP8dYxxf+hztHte0/xUXFxNCoKioiKKiIpYvX87111+f2i4qKqJ58+Z069at3LYA//jHPxg6dChvvfUW999/PwMGDADggw8+4JprruHWW2/lwgsvpHv37sQYdziWBx98kKVLl7JixQpOPPFE7rvvPgBuv/12+vXrR1FREX379mXgwIEA3HDDDZxwwgn8+9//pnfv3gwaNGgf3y1JkiRJB9peCbNKvsLCQvLy8na4/6WXXqJevXocf/zxO2z71FNP0a9fP7KysmjTpg2rV69m/fr1fPTRR9x///20aNGC7t278+WXX/LFF1/s8FwTJ05k0KBBpKWlceWVVzJp0iQA5s+fT5s2bQBo164dK1aUvAj79NNP54477qBGjRp06dIlFa4lSZIkHbwMswKgoKCAV199laysLI488kgmT568zf57772XX/ziFzttu3z5co477rjUMVlZWSxbtow2bdrQsWNHvvrqq1SoPeSQQ3Y4lrL91KpVi61fx9SsWTP++Mc/sm7dOh566CE6duwIQM+ePalYsSIAL774Iq1b++i1JEmSdLDbnRdA6SBUs2ZNhg0bRt++fZk9ezbdunWjU6dOVKlSheXLl/PBBx/Qrl27nbbdvHkz6enpqT5r1KjBmjVrUtvXX389f/jDH5g4ceJOx/LNfr7++msALrnkEtq0acOcOXP48MMPeemll7Y5bvXq1dxzzz288MILe3w/JEmSJH2/GWYFQL9+/VKfW7duTcOGDSksLKRVq1Y8+uij9OzZc5dt69Sps0143bBhAxUq/Gfy/+677+bCCy/k9NNPZ/HixTucnd3aT40aNQD48ssvgZIw+/TTT9OmTRsWLVrEueeeS0FBQeq4AQMG8LOf/YxmzZrt4d2QJEmS9H3nMmMBMGbMGDZt2pTaXrFiRSqIPvHEE5xzzjm7bJufn8/rr78OQIyRt956i/r16/P222+zePFiAPLy8sjKyuJf//rXDsdStp8lS5ZQvXp1AGbPnk1ubi4AP/rRj1i+fDkbNmwAYOTIkSxevJibb755j++FJEmSpO8/Z2YFwIwZM1ixYgXXXHMNo0ePJi0tjby8PD799FOWLFmyzbOwO2p71FFHMXDgQE477TTeeecdDj30UHJycnj55ZcZPXo0zz33HIsXL6aoqIgmTZrscCy9e/emf//+1KtXj3vvvTcVpBs3bsx9991Hr169mDRpEunp6VSrVo3XXnuN3/zmN7zxxhukpaXt83slSZIk6cBzZlYA3HbbbUydOpWMjAzGjx/P5MmTqVKlCjNmzKBVq1bfqm16ejoTJkzg7rvvZvr06YwdOxaACy64gGbNmtGwYUN69erF448/TlpaGrNmzaJLly7bjaVt27YMGjSIa665hkMOOYQbb7wRgFGjRvG3v/2N5s2bM3r0aMaMGQPA7373O9atW0ebNm22+95bSZIkSQensLPv+/w+ys/Pj3Pnzj3Qw9ipRjdMOdBD2KWld2wfIiVJOhAOpr83D6ZrOdj4eyMlRwhhXowxf1ftXGasnfIPfkmSJEnfRy4zliRJ0jaaNm1KZmbmNo/vlFfbWf3BBx8kMzOT/Px8lixZAsCWLVuoVatWqn2bNm12OZaxY8dyxhlncN555/H+++8DsGnTJs4991wOP/xw8vLyeOONN/bm5UtKCGdmJUmSlFJcXEwIgaKiop3Wdlb/xz/+wdChQ3nrrbdYtmwZAwYMYMqUKSxcuJDWrVvz/PPPf6uxvPbaa9xzzz385S9/4cUXX6Rfv368/vrrPPzww+Tl5fHkk0/y7LPPcvHFF/POO+/s+cVLShRnZiVJkpRSWFhIXl7eLms7qz/11FP069ePrKws2rRpw+rVq1m/fj0FBQU0b978W49l7dq1jBw5kqZNm3LhhReycOFCAObPn5+a1W3Xrh0rVqz4Lpco6SBhmJUkSVJKQUEBr776KllZWRx55JFMnjy53NqO2gIsX758m6/1y8rKYtmyZRQUFPD444+TmZlJs2bNUt8rvyOdO3emRYsWbNiwgaFDh9KjRw8AmjVrxiOPPMK6desYMWIEHTt23Ed3Q9L3mWFWkiRJKTVr1mTYsGGsXLmSsWPHcumll1KjRo3tahs3biy37caNG9m8eTPp6empPmvUqMGaNWvIyMjg4YcfpqioiJtuuolLL730W42pX79+/P73v6d///4A9OzZk9dee43OnTvz+9//nl/96lf75F5I+n7zmVlJkiSl9OvXL/W5devWNGzYkKZNm6a+d35rrbCwsNy2hYWF1KlThzVr1qT2bdiwgQoVKnDdddelaj169ODKK6/k448/5rDDDtvpmP72t7/xwgsv0KNHDxYvXszAgQP5n//5H3r37s3q1atp27Yt8+bNo1q1anvrNkhKAGdmJUmSlDJmzBg2bdqU2l6xYgVTp07drlahQoVy21aoUIH8/PzUEuIYI2+99Rb169fn4YcfTrX98ssv+eyzz6hQYcf/HJ05cyYff/wxAB07duSzzz6juLiY2bNnk5ubC0DdunWpUaMGS5cu3SvXLyk5DLOSJElKmTFjBnfddRfFxcUMHz6ctLQ0lixZsl0tLy+v3LZ5eXmcddZZPPnkk0ycOJGhQ4dy6KGHkpOTw7hx4xg7dixffPEFN998MyeddBJ169bd4Vhefvllrr32WjZv3swLL7xA/fr1qVGjBo0bN2bEiBEsWLCAMWPGsHjxYho0aLAf75Kk7wPDrCRJklJuu+02pk6dSkZGBuPHj2fy5MkMGzZsu1qVKlXKbVulShXS09OZMGECd999N9OnT2fs2LEADB8+nN/97nfUr1+ff/zjHzz22GMAzJo1iy5dumw3ll/96lds3ryZrKwsBg8ezOOPPw7A3Xffzdtvv82JJ57Ib3/7W/70pz9Ro0aN/XeTJH0v+MysJEmSUg477DCmT5++Xb282o7aArRp04ZXX311m9rRRx9NQUHBdm1PPvlkpkyZsl29atWqqQBbVm5uLrNnz97hNUj6YXBmVpIkSZKUOM7MSpIkqVyNbth+tvT7Zukd2y9PlvTD4MysJEmSJClxDLOSJEmSpMQxzEqSJEmSEscwK0mSJElKHMOsJEmSJClxDLOSJEmSpMQxzEqSJEmSEscwK0mSJElKHMOsJEmSJClxDLOSJEmSpMQxzEqSJEmSEscwK0mSJElKHMOsJEmSJClxDLOSJEmSpMQxzEqSJEmSEscwK0mSJElKHMOsJEmSJClxDLOSJEmSpMQxzEqSJEmSEscwK0mSJElKHMOsJEmSJClxDLOSJEmSpMQxzEqSJEmSEscwK0mSJElKHMOsJEmSJClxDLOSJEmSpMQxzEqSJEmSEscwK0mSJElKHMOsJEmSJClxDLOSJEmSpMQxzEqSJEmSEscwK0mSJElKHMOsJEmSJClxDLOSJEmSpMQxzEqSJEmSEscwK0mSJElKHMOsJEmSJClxDLOSJEmSpMQxzOqgNnr0aH72s5+lttesWUODBg344IMPUrUhQ4bQsGFDmjRpwsSJE7c5fvr06Zxxxhnb9btgwQLy8vK+1Rief/55GjRoQFpaGn379uWrr77aZn+nTp149NFHU9tz5swhNzeXww47jN69e1NcXPytziNJkiT9kBhmddD65JNP+OUvf7lNbciQIfTu3ZsjjjgCgGeeeYZZs2axYMECpk2bxoABA1i6dCkAb775Jn369OHrr7/epo9FixZx7rnnsm7dul2Oobi4mP79+/PII4/w0UcfMX/+fMaPH5/aP2HCBNauXUvv3r1TtZ///Oc89dRTrFy5khACDzzwwO7eAkmSJOmgZZjVQevaa6+lY8eOqe2CggImTpzI4MGDU7Vx48Zx8cUXU61aNRo3bkyLFi2YO3cuACNGjGDo0KHb9fvAAw9w6623fqsxfPHFF/zxj3+kQ4cO1KlTh1NPPZXly5cDsH79eq677jruvfdeQgipY4qKijjqqKOoVKkSp5xyCitWrNit65ckSZIOZoZZHZReeOEFli9fzmWXXZaqXXPNNaSnp9O3b19GjBhBjBGA1atXA7B582beffdd6tatC8Cf/vSn1AxuWXfddRctWrT4VuM47LDDOP300wGIMfLSSy/RunVrAIYNG8a6deu4/fbbuf766/niiy9Sxzz55JN8+umnjB07dptALkmSJKlEpQM9AGlv27BhAwMHDmTixImpWc1Zs2Yxa9Ys7rzzTnJycrjhhhsIIdC7d28uuOACvvjiC2bOnElxcTGnnnoqwDazpWXtqL4rY8aM4ZBDDqFdu3Z8+eWX3HfffZx33nl0796d3//+91xyySU8/vjjXH311VxxxRX86Ec/Ii0tjdNOO233boQkSZJ0EDPM6qBz8803c8EFF9CkSZNUmH3jjTfo2rUr1157LVCyxHf8+PFMmTKFCRMmMHXqVObMmcOvf/1rKlXa+/9brFixgv/+7/9m6tSpALz77rtUrlyZkSNHUrFiRY499lhyc3NZu3YtN998M++99x61atXikUceYdCgQfzhD3/Y62OSJEmSkswwq4PO008/zerVq7n77rvZtGkTGzZsYNKkSfTr1y/VpmrVqtSuXRuADh06ULduXf76179yxRVX7PXxfPXVV/Tu3Ztf/vKXHHfccQCkp6dTv359KlasuM145s+fT82aNalVqxYA+fn5vgBKkiRJKofPzOqg889//pOPPvqIoqIiJk6cSK9evZg9ezZTp07lk08+YcOGDYwaNYp27dqljrn99tsZMmQI1av/f/buPc7HOv//+ONCKnKo2JllBqMUcqrGqYTslrBSfiKJkoqiFTqwajt+xRZfrW+ifHfU9yspJqmw27eNVaLJqaNRiZymTJuIcVhdvz9mfNaYg2NmLj3ut5vbbT7vz3W9P6/rMp/5fJ7X+7reV5ljXs/vf/97zjzzzNioMEBSUhI//vgj77//PgDjxo2jVatWVKtWjfT0dGbNmsUnn3zCo48+Sr169Y55TZIkSVLUGWb1i3DuuecyfPhwLr74YhISEqhatSp9+vQB4OOPP2bJkiXcfPPNR/UanTt35u23387VtmXLFiZMmMD8+fOJj48nPj6eIUOGUKJECV588UX69+9PpUqVmD9/Pk888QRVq1ZlzJgx9O/fn8aNG5ORkcF//Md/HFVdkiRJ0onI04x1QmvdujWtW7cGoFevXrlONd6nXr16pKenF7j+vHnz8rTXqFEjdj/afVJTU/MsV7FixdisyQdq3LgxaWlpedr79etHv3798l1HkiRJUjZHZiVJkiRJkePIrH4xagx9o6hLOKg1IzsU+nxKSgrz589n8uTJ1KlTh++//x6AUqVKsX79egAmTJjAgw8+SEJCAi+//DJJSUkA/PGPf2TChAnUr1+fl19+mTPOOIPdu3fHrimuVKkSkyZNomnTpoXW8OSTTzJz5kyqVavGn/70J+Li4ti+fTs9evRg4cKFdOnSJdekVVlZWTRr1oxXX32VGjVqHMXekSRJkv7NkVkpIjZv3szdd98NwI4dOwiCgIyMDDIyMmJB9qOPPuKRRx5h6dKljBs3jgEDBgDwxhtv8Morr/DFF19w8803c9999wHw7LPPUr9+fTZt2sSoUaNi1xEXZMqUKcydO5dnn32WX//619x5550APPjgg5x++ulkZGTw008/8dJLLwHZt0Dq0qULn3766c+yTyRJ+qVISUnhxhtvLLTt4YcfJiEhgYSEBFJSUnItu2XLFqpVq8bq1atjbc8//zyVK1emTJkyDBky5JDqmDJlCr17987V1rZtW+Li4mLzg2zatOmI+5cOh2FWiohBgwZx2WWXAbBixQrq16+fZ5mZM2fSq1cvqlSpQvPmzcnMzGT79u2kpqYyYMAAypcvz7XXXsuCBQsA+PTTT2nevDkArVq1it2XtyA//fQT48eP5+yzz6ZHjx6sWrUKyL5eeOjQoZQoUYLbb7+dmTNnAjBt2jS6du1K1apVj9l+kCTpl2b/A9oFtX388cfMnz+ftWvX8t5773HvvffyzTffxJ6/77776N69OzVr1gTg66+/5sEHH+TNN9/k888/Z9q0abz77ruF1vH666/Tv3//PPOBrFq1ik2bNsUOsv/6178+ov6lw2WYlSLgzTffZP369dx6660ALFu2jHfeeYcqVapw1llnMWvWLADWr18fu5ctQJUqVVi7dm2u9iAIOOmkk9i+fTv16tXjueeeY9u2bTz99NOxsFyQnj17kpSUxA8//MCoUaPo0qULAJmZmZxzzjkAVK9enS+++AKA3r17c8MNNxzbnSFJ0i/M/ge0C2r77LPPSE5OpmTJkiQmJlK9enUyMjKA7O8NqampDB8+PLb8v/71L6ZOnUqjRo2oWrUqF1xwQexMr4KkpKRw//3352rbtGkTv/rVryhRInesOJL+pcNlmJWKuaysLAYPHsyzzz5LEAQAnHbaaYwaNYqNGzcyZcoUbrnlFnbt2sXevXspX758bN2yZcuyZcuWPO1lypThhx9+oGvXrrz77ru0b9+eP//5z9xzzz2HVNPll1/O3Llzue6662L97att32sCsTZJknRkDjygXVBb3bp1mTVrFps2bWLBggVkZmZSp04dAAYOHEj58uW5/vrrefrppwnDkJo1a8bmydixYweLFi2iSZMmhdYyffp0zjzzzFxty5Yt46uvviIhIYHExESeffZZgCPqXzpcTgAlFXMPPfQQPXv2pFatWrHTgPe/xVCzZs2oXr06K1as4PTTT48FScgOwiVKlCiwffDgwfzpT3+ie/fuZGZm0rJlS5YsWcKpp55aaE2LFy/m2Wef5YYbbmDevHmUKFGCMAwJgiDWtyRJOjr7DminpqbGvgPk1wbZYTYxMZEOHTqwbds27rrrLkqXLs3ChQtZuHAhjz/+OImJiQwdOpQgCHLdBvDxxx+nTZs2sUkjC5LfQeoSJUowfPhw7rjjDr788ksuuugiOnbsSHx8/GH3Lx0uv3FKxdxrr73G6NGjiY+Pp3PnzkybNo0SJUqwe/fu2DIbNmygRIkSJCcn89577wEQhiFLly6latWqudp37NjBV199xZlnnsmiRYuoW7cuAJUqVaJs2bJ57p+7v9mzZ5OVlQXANddcwyeffAJAgwYNWLJkCQAffPABCQkJx3w/SJL0S7P/Ae3C2gAmT55MUlISS5cuJT09nZkzZ5KWlsbixYvp2LEjgwYNokuXLtx///289tprsfVWrFjBhAkTGDNmzBHVeMUVVzBw4EBKlChBrVq1uPTSS/nHP/5xzPqXCuPIrFTM7QuMAPPmzWPy5MmUKlWK0aNHM3DgQFJSUihdujT169fnnHPOYfDgwbRp04aPP/6YM888k8TERLp06ULr1q1p0KABr7zyCm3atOGkk04iKSmJp59+mjvvvJP333+fL7/8kmrVqhVYy/Tp01mwYAGPPfYYL7/8cux0oeuuu44777yTkSNHMnz48Njpx5Ik6ci99tprZGZmMmbMGHbv3k1WVhY7d+7kV7/6Va62rVu3Urly5dgB6hIlStCoUSM++OADypcvn+uz/ZRTTqFixYoAbN26lW7dujF+/HiqVKlyxDU2adKEuLg44N8H2I9V/1JhHJmVImjEiBHMnTuXuLg4XnrpJWbNmsXJJ59M+fLlmT59OmPGjGH+/PlMmTIFgLPOOovx48fzhz/8gY0bN/Jf//VfAIwZM4bly5dzwQUX8Mc//pG//OUvlC1blmnTpuU6/WifP/3pTyxdupRf/epXvPjii0yYMAHInhiqQ4cO3HbbbTRu3DjfdSVJ0uH55JNP+Oabb8jIyCA1NZVu3boRhmGettTUVJKSkpg2bRorVqxg3rx5TJ06lXr16tGiRQvmzp3L5s2bycrKYtKkSbRq1QqA7t27c8UVV3D11VcfcY0rVqzgoYceYvv27cycOZOVK1ce0/6lwjgyK0VI69atad26NQDz58/Pd5nmzZvzzjvv5Gnv2LEjHTt2zNVWt25dFi1alGfZbt260a1btzztlSpV4q9//Wu+rzts2DCGDRuW73OFnbosSZKOXv/+/VmyZAktW7bkpJNO4rbbbuOSSy4BYPjw4Vx88cV89913dOzYkT59+rBixQpmz55NpUqVePHFFwEYOnQod955J/Hx8axdu5aTTz75oK87ZMgQunfvTlxcHOeeey6pqalUrly50P6lY8UwK0mSJBVz+x/Qzq+tXLlyvPzyy/mu26tXr1yTRwI0bNgwz/1i99l3S5/83Hjjjdx4442xx6eeemrs/vKH2r90rBhmpYiqMfSNoi7hoNaM7FDUJUiSJOkEZZiVJEmSIiQKB7TBg9r6+TkBlCRJEZCSkhI7tW/kyJFUqFCBihUrMnr06LpSO7wAACAASURBVNgyDz/8MAkJCSQkJJCSkgLA7t276du3LwkJCdSvX58FCxYU2ndh/vrXv9KuXTs6duzI+++/D8BPP/1EhQoViI+PJz4+nubNmwPwww8/0LVrV6pUqUKzZs1yzcwuSdKx4MisJEnF3ObNm7n77rv53e9+x3vvvcfUqVNZtmwZO3fupFmzZnTt2pUffviB+fPns3btWjZu3MiFF15I+/btefnll9m+fTtr1qxh+fLldOzYkVWrVlGuXLk8fRdm9erVDBw4kClTprB27Vo6d+7MunXrWLVqFc2aNcszOdwjjzzC2WefzbRp05g1axadO3cmPT39Z9tHkqRfHkdmJUkq5gYNGsRll10GQJkyZXjhhReoWbMmdevWpUaNGmzcuJHPPvuM5ORkSpYsSWJiItWrVycjI4Np06Zx2223UapUKZKTk6lUqVKuULl/34X55ptvGDduHBdeeCGdO3dm586d/PjjjyxbtoxGjRrlWX7atGn079+fIAjo1KkTGzduJDMz89jtFEnSL55hVpKkYuzNN99k/fr13HrrrUD2DKHnnXceABs3bmTdunXUq1ePunXrMmvWLDZt2sSCBQvIzMykTp06ALEQuW3bNtauXUulSpXy7bswzZs357LLLmPPnj2xUFuuXDmWLVvGiy++SHx8PPXq1eO9996LrbPvddesWcOePXtio8GSJB0LnmYsSVIxlZWVxeDBg0lNTWXDhg15nh8+fDh9+/albNmy1K1bl8TERDp06MC2bdu46667KF26ND169GDAgAGsXLmSV155hdq1a1OjRo2D9l2Qe++9l//6r/8iNTUVgLi4OJ599lkuv/xypk+fzi233MLHH39Mjx49uO666+jVqxfPPfccV1555SHds1KSpEPlyKwkScXUQw89RM+ePalVq1ae5+bMmcM777zD/fffD8DkyZNJSkpi6dKlpKenM3PmTNLS0ujXrx9//vOf2bx5M0uXLuXee+89aN+FGTNmDEuWLOGmm25i27ZtDBkyhMsvvxyALl268O233/Ltt98yYsQI7rnnHtauXctnn33GPffcc5R7Q5KKpwMn0ZsyZQq9e/fOtcykSZOoXr06cXFxjBgxAoAXXnghNnlefHw8FSpU4KabbgLg+eefp3LlypQpU4YhQ4YcUh1btmyhWrVqrF69Otb2xRdf0K1bN37zm9/wP//zP7mW/9e//kW9evV49913j2SziwVHZiVJKqZee+01MjMzGTNmDLt37yYrK4utW7fy5JNPcvPNN/PKK69QtmxZABYtWkTdunUBKFGiBI0aNeKDDz6gcePGXH311ZQqVYq33nqLzp07F9r3vhHXAy1fvpxy5cpx1llnUb9+fapUqcLXX3/NwoULueWWWwDYuXMn//znPylRogQlSpTghhtuYOvWrXTq1Ink5OTjsMck6fg6cBK9119/nf79+3PVVVfFlvnuu+946qmn+Oyzz9i1axfNmjXj8ssv57rrruO6666LLXfddddx1VVX8fXXX/Pggw/y5ptvUrlyZZo2bUrnzp25+OKLC63lvvvuo3v37tSsWROAvXv30rlzZx588EFq1KhBp06daNy4MbVr1wZg7NixnHfeeQfttzgzzEqSVEztfzubefPmMXnyZCZOnEizZs24++67adKkSez5pKQkpk2bRuvWrfn++++ZOnUqU6dOjT3/2GOPMWLECIIgKLDvyZMnF1jLhx9+SEpKCnPmzOHLL78kIyODWrVqMXDgQMqUKUOnTp34j//4D5o2bRq7Jnf37t2MHTuWV1999VjtEkkqVg6cRC8lJYX777+fjz76KNa2evVqzjnnHMqUKUOZMmVo0KABGzZsyHWQ7/PPP2fVqlVceeWVrF69mqlTp8Ym17vgggtYv359oXUsW7aM1NRUVq5cGWvLzMxkwIABsYOYTZo04fPPP6d27dps3LiRP/3pT7HbrEWVpxlLkhQhc+fOZfny5YwcOTJ2atqMGTPo378/VatWpWXLlnTp0oXevXtzySWXADB79mxKly5Nu3btDtr/unXraNCgQZ72nj17Uq9ePapXr063bt148cUXKV26NE899RRPPPEEVatW5aOPPsoVoCdNmsQll1xCvXr1jt0OkKRiIr9J9KZPn86ZZ56Za7mzzjqLhQsXsmrVKj799FMWLVrERRddlGuZcePGcccddwBQs2ZNmjZtCsCOHTtYtGhRroOX+Rk4cCDly5fn+uuv5+mnnyYMQ+Li4rj11lsJw5D58+ezYsUKWrZsCcDQoUM5+eSTueOOOxgxYgR79uw56v1RFByZlSQpAlq3bk3r1q0BCMMw32VefvnlfNvbt29P+/btD6nvxMREPvzwwzzLBEHAuHHjGDduXK72c889l2XLluXb7+23387tt99e4OtKUlQVNInevrNf9nfGGWfQtm1bOnXqRKlSpbj22mupXLlyrr5ef/11Ro0alWfdxx9/nDZt2pCUlFRgLQsXLmThwoU8/vjjJCYmMnToUIIgoF+/fgC89NJL3HLLLfTt25cKFSqwbt06pkyZwr333kvz5s15+OGH2bp1KyNHjjyaXVIkDLOSJEVQjaFvFHUJB7VmZIeiLkGSfhb7T6J3sBnh3377bTZs2MCnn35KEAT07t2b6dOn06VLFyB7DoPWrVtz6qmn5lpvxYoVTJgwgSVLlhTa/+LFi+nYsSODBg0CYPv27bz00kuxMNutWzfatm1LixYt6NChA99//z0NGjSITURVvnx5br/99kiGWU8zliRJkqTD8NprrzF69Gji4+Pp3Lkz06ZNi12beqBFixZRu3bt2KhtcnIyaWlpsednzJiRa8IogK1bt9KtWzfGjx9PlSpVCq2lfPnyVKtWLfb4lFNOoWLFimzatCl27++KFSvy29/+lk8++aTA5aPIMCtJkiRJh+GTTz7hm2++ISMjg9TUVLp161bgbPBJSUm8/vrrvP/++yxevJiJEyfG5hL46aef+L//+7/YpR77dO/enSuuuIKrr776oLW0aNGCuXPnsnnzZrKyspg0aRKtWrVi586ddO7cmfXr17N161b+9re/0aRJEy644AKWLVvG6tWr2bt3L+PHj6dVq1ZHvU+KgmFWkiRJkn4m11xzDW3atOGKK66gbdu2NGnShB49egDZpxL/+te/pnz58rHlV6xYwezZs5kyZUpsor+xY8cCEB8fz65du3L1f+655zJ8+HAuvvhiEhISqFq1Kn369CEpKYnHHnuM5s2bU79+fW6//XYaN27M6aefzsSJE+nUqRNxcXF89913/OEPfzh+O+QY8ppZSZIkSTpC+0+iB3DjjTdy4403xh6XLFmSiRMnMnHixDzrnn/++Xz88ce52ho2bFjgRH8ZGRn5tvfq1YtevXrlaT+wln3atWt3SDPcF3eOzEqSJEk6rlJSUmIha8KECcTHx5OcnMxXX30VW+aOO+6gbNmyxMXF8cILL+Tp44EHHuDBBx885Pb97d27l1tuuYXKlStzzTXX5BnthOyQOm/evFxtL7zwQq77yqpoOTIrSZIk6bjZvHkzd999N7/73e/46KOPeOSRR1i6dClr165lwIABvPHGG0ydOpXPPvuML7/8kvT0dDp27Mj/+3//j5NPPhmAlStXMnLkSIYNG5ar74LaDzRhwgTWrFnDhg0bGD16NE8++ST33HNP7Pm//OUvzJ8/P9c627ZtY+jQocyePbvAfp1p/vhyZFaSJEnScTNo0KDY6ObMmTPp1asXVapUoXnz5mRmZrJ9+3YSExNJSUkhPj6eVq1aEQQBW7ZsAbLvtd23b988kyMV1J6f1NRU7rrrLkqXLs3tt9/Oq6++Gnvu22+/ZcSIEbRt2zbXOg888AAdOnSITd6komeYlSRJknRcvPnmm6xfv55bb70VgPXr19OgQYPY81WqVGHt2rW0aNGCxMREAN5//33OOOMM4uLiAJg0aRLVq1fniiuuyNV3Qe352f91K1SowObNm2PPDRo0iD/84Q/Ex8fH2tLT0xk3bhxffvkl3bt354MPPjjCPaBjyTArSQIgLS2NTp060bZtW+bMmcPu3bvp27cvCQkJ1K9fnwULFuRZZ/9rnkaNGhWbdTE+Pp7TTjuNhx9++JD6OdDhXif117/+lXbt2tGxY0fef//9I94HkqSfT1ZWFoMHD+bZZ5+N3XN17969uWbyLVu2bGwEdp+hQ4dy7733AvDNN98wevTo2Oy++xTUXpADX/df//oXAH/729/49ttvuemmm3ItP3r0aM4++2xuu+02GjZsyG9/+1u+/fbbQ9xy/VwMs5IkfvjhB3r06MGdd97JsGHDuOGGGxg1ahTbt29nzZo1pKSk0LVrV7Zt2xZbZ981T/vce++9ZGRkxP41atSITp068cwzzxTaz4H2v07qpZdeol+/frkm5th3PdQ+q1evZuDAgTz66KP07t2bzp07FzgLpCSp6Dz00EP07NmTWrVqxdpOP/30XOE1KyuLEiX+HVGefvppdu/eHRvJHThwIA8//DBnnHFGrr4Lai/Iga+7c+dOsrKyuOuuu3jmmWfyLL948WJGjhzJ1VdfzdChQzn//PPzXFOr488JoCRJbNq0iUcffZRLL70UgMTERMaMGcPrr79OqVKlSE5OplKlSqSnp5OcnAzkvubpQH//+9+pXLkyDRs2ZMCAAYwcObLAfg60/3VS8fHxseuk4uLi8r0e6ptvvmHcuHFceOGFXHjhhdx66638+OOPlCtX7hjvJUnS0XjttdfIzMxkzJgx7N69m6ysLHbu3En//v3p0aMHYRiydOlSqlatCsDy5ct5+OGHWbhwYSzgvvHGG8ybN4/f//73ZGVlsXfvXr7//vsC25988sl8a0lOTua9996jS5cufPXVV5QpU4YlS5bw9ddf07x5cyD7QO+sWbN4+umnKV++PNWqVYutf8opp1CxYsWfeY/pYAyzkiRq165N7dq12bt3LzNnzmTPnj3Uq1ePzMxMIHsGx7Vr11KpUiXg39c8PfDAAzz33HN5+hs7dixDhgyJPS6on/y0aNEi9nNB10m1adOGNWvWAMS+dOzZs4cJEyZw4YUXGmQlqRj65JNPYj/PmzePyZMn8+c//5k6derQpk0bPv74Y84880wSExP57rvv6NixI+PHjycpKSm23v5n9kyePJk1a9bw4IMP5gqt+7cXpHv37vTt25fKlSszduxYrrrqKlq0aJFrtHbfPVpbt27Nhx9+yPPPP0+jRo346KOPSEtLK/CgrI4fTzOWJMU8+eSTXH/99fTt25cePXowYMAARo0axWWXXUbt2rWpUaNGvtc87W/9+vWsXr2aVq1aARTYz6E4lOuk9rn33nsZMmQId9xxx5FtvCTpuCtfvjzTp09nzJgxzJ8/nylTpgDwv//7v2zcuJHbbrstdqbOkcyJsHDhQjp0yHsrmpYtW3LXXXcxcOBAypUrxwMPPFBoP8OHD2fLli1UrVqVdu3aMWHCBE4//fTDrkfH1hGPzAZBEAfMDcPw/CAI/huoC7wRhuGjOc8fcZskqWgMHjyYrl270rRpU+bPn09cXBzvvvsuS5cuZerUqUDua542bNiQp48XXniBrl27xh7369cv334O5lCvk9pnzJgx9O7dm9/85jd8+eWXjs5KUjHWunVrWrduDWSfYfPOO+/ken7gwIEMHDiw0D72TUBYWPtFF13EG2/kf+/XPn360KdPnwL7nzx5cuznMmXKkJKSUmg9Ov6O5jTjJ4BTgyDoDJQMw7B5EAR/CYKgFlD/SNvCMPz8qLdKknRYvvjiC3bs2EGDBg1ISEigcePGpKenc/XVV1OqVCneeustOnfuDOR/zdPWrVtJTU0FYMaMGTz77LO5+s+vn8IcznVSvXv3ply5cpx11lnUr1+fKlWq8PXXX3Peeecd470kSZKKkyMKs0EQtAG2AxlAa+ClnKf+BrQAzj+KNsOsJB1nGzdupG/fvrz33ntkZWWRlpbG+PHjAXjssccYMWJE7JTi/K552nf0+rvvvuOrr77Kdc/AfQ7spyCHe53U888/T0pKCnPmzOHLL78kIyMj10yZkqTircbQ/EdOi5M1I/Oeqqyid9jXzAZBUBq4Hxia01QW2Hee2T+BuKNsy+81bw2C4IMgCD7Y/4bGkqRjo2XLlvTq1Ys6depwySWXMHbsWKpUqcLs2bMpXbo07dq1O6R+/vGPf9CkSZM87fn1s27dunxD7+FeJ9WzZ0/q1atH9erV6datGy+++CKlS5c+pHolSVJ0HcnI7FBgfBiGW3KOrv8InJrz3GlkB+SjacsjDMNngGcAkpOTvXmgJP0Mhg0bxrBhw3K1tW/fnvbt2xe4zv7XPEH26cT73zansH4SExP58MMP8yx7uNdJBUHAuHHjGDduXKHrSJKkE8uRzGb8W6B/EATzgEZAR7JPDwZoCKwBlhxFmyRJkiRJhTrskdkwDFvu+zkn0F4JLAiCoArQDmgGhEfRJkkqJryOSZIkFVdHdZ/ZMAxbh2G4lexJoBYBl4Zh+MPRtB1NPZIkSZKkX4ajuTVPTBiG3/PvWYmPuk2SJEmSpMIc1cisJEmSJElFwTArSZIkSYocw6wkSZIkKXIMs5IkSZKkyDHMSpIkSZIixzArSZIkSYocw6wkSZIkKXIMs5IkSZKkyDHMSpIkSZIixzArSZIkSYocw6wkSZIkKXIMs5KkE05aWhqdOnWibdu2zJkzB4A6deoQHx9PfHw8CQkJuZafMmUKvXv3ztU2adIkqlevTlxcHCNGjIi133fffVSvXp1atWqRmpp60FoefvhhEhISSEhIICUl5YhfV5Ik5VaqqAuQJOlY+uGHH+jRowcTJ04kCAK6du3KsmXLCIKAjIyMPMu//vrr9O/fn6uuuirW9t133/HUU0/x2WefsWvXLpo1a8bll19ORkYGCxcuZOXKlWRkZHDxxRdzwQUXUKNGjXxr+fjjj5k/fz5r165l48aNXHjhhbRv3564uLjDet3k5ORjvp8kSYo6R2YlSSeUTZs28eijj3LppZfSunVrEhMTWb16NfXr1893+ZSUFO6///5cbatXr+acc86hTJkynH766TRo0IANGzYwbdo0+vTpw6mnnkpSUhIXXnghH3zwQYG1fPbZZyQnJ1OyZEkSExOpXr16LFAfzutKkqS8DLOSpBNK7dq16dq1K3v37mXGjBns2bOHDz/8kHfeeYcqVapw1llnMWvWrNjy06dP58wzz8zVx1lnncXChQtZtWoVn376KYsWLeKiiy4CIDMzE4C9e/fy2WefUalSpQJrqVu3LrNmzWLTpk0sWLCAzMxM6tSpc0SvK0mScvM0Y0nSCenJJ59k+PDhPPHEE5QrV45Ro0Zx/fXXs2jRotj1tCeffDJBEORZ94wzzqBt27Z06tSJUqVKce2111K5cmW6d+9Oz549+fHHH1mwYAE7duygRYsWBdZQt25dEhMT6dChA9u2beOuu+6idOnSAIf1upIkKS9HZiVJJ6TBgwfz+eefM2LECC666CKuv/56AJo1a0b16tVZsWJFgeu+/fbbbNiwgU8//ZSPPvqIzMxMpk+fTvv27Zk+fTpbt24lLS2NIUOGUKpUwceFJ0+eTFJSEkuXLiU9PZ2ZM2eSlpZ22K8rSZLyMsxKkk4oX3zxBR9++CEACQkJNG7cmHfffZfdu3fHltmwYQMlShT8Ebho0SJq164dGz1NTk6OhdBLL72U66+/nlNOOYXbbrut0FoWLVpE3bp1AShRogSNGjUq9Brbwl5XkiTlZpiVJJ1QNm7cSLdu3diyZQubNm0iLS2N+fPnM3r0aHbs2MFTTz1F6dKlC5wQCiApKYnXX3+d999/n8WLFzNx4kTq1asXe/6xxx7jvvvuo0yZMoXWkpSUxLRp01ixYgXz5s1j6tSpufo53NeVJEn/5jWzkqQTSsuWLenVqxd16tShbNmyjB07llatWnHNNdcwYsQILrjgAmbNmsXJJ59cYB/XXHMNb7/9NldccQU//fQTXbp0oUePHkD27XaWLFnCc889l2ud+Ph41q5dm6vf/v37s2TJElq2bMlJJ53EbbfdxiWXXHJErytJknIzzEqSTjjDhg1j2LBhudrmz59f4PI33ngjN954Y+xxyZIlmThxIhMnTsyzbL169UhPT8/Tnt89bMuVK8fLL798TF5XkiTl5mnGkiRJkqTIcWRWkvSLUGPoG0VdwkGtGdmhqEuQJCkyHJmVJEmSJEWOYVaSJEmSFDmGWUmSJElS5BhmJUmSJEmRY5iVJEmSJEWOYVaSJEmSFDmGWUmSJElS5BhmJUmSJEmRY5iVJEmSJEWOYVaSJEmSFDmGWUmSJElS5BhmJUmSJEmRY5iVJEmSJEWOYVaSJEmSFDmGWUmSJElS5BhmJUmSJEmRY5iVJEmSJEWOYVaSJEmSFDmGWUmSJElS5BhmJUmSJEmRY5iVJEmSJEWOYVaSJEmSFDmGWUmSJElS5BhmJUmSJEmRY5iVJEmSJEWOYVaSJEmSFDmGWUmSJElS5BhmJUmSJEmRY5iVJEmSJEWOYVaSJEmSFDmGWUmSJElS5BhmJUmSJEmRY5iVpGNg5MiRVKhQgYoVKzJ69GhGjRpFfHx87N9pp53Gww8/HFt+y5YtVKtWjdWrV+fp6+233+bSSy+NPX7++eepXLkyZcqUYciQIYXWsXv3bvr27UtCQgL169dnwYIFsefS0tLo1KkTbdu2Zc6cOcdgqyVJkopOqaIuQJKi7r333mPq1KksW7aMnTt30qxZMz755BPuvffe2DItWrSgU6dOscf33Xcf3bt3p2bNmrn62rVrF3379qVKlSoAfP311zz44IO8+eabVK5cmaZNm9K5c2cuvvjifGt55pln2L59O2vWrGH58uV07NiRVatW8dNPP9GjRw8mTpxIEAR07dqV9PR0Tj/99J9hj0iSJP38DLOSdJTKlCnDCy+8EAumNWrUYOPGjSQmJgLw97//ncqVK9OwYUMAli1bRmpqKitXrszT1yOPPMKFF17Ipk2bAPjXv/7F1KlTadSoEQAXXHAB69evL7CWadOmMXLkSEqVKkVycjKVKlUiPT2d0047jUcffTQ24puYmMj69esNs5IkKbI8zViSjlLDhg0577zzANi4cSPr1q2jXr16sefHjh3LnXfeGXs8cOBAypcvz/XXX8/TTz9NGIYAfPrpp7z66qs88MADsWVr1qxJ06ZNAdixYweLFi2iSZMmhdaTmZkJwLZt21i7di2VKlWidu3adO3alb179zJjxgz27NlD3bp1j80OkCRJKgKOzErSMTR8+HD69u1L2bJlAVi/fj2rV6+mVatWACxcuJCFCxfy+OOPk5iYyNChQwmCgL59+9K3b1+eeuopTjnllHz7fvzxx2nTpg1JSUkFvn6PHj0YMGAAK1eu5JVXXqF27drUqFEj9vyTTz7J8OHDeeKJJyhZsuSx23BJkqTjzDArScfInDlzeOedd1i+fHms7YUXXqBr166xx4sXL6Zjx44MGjQIgO3bt/PSSy8BUL9+fVq2bMmaNWvy9L1ixQomTJjAkiVLCq2hX79+xMXF8e6777J06VKmTp2a6/nBgwfTtWtXmjZtStu2bTn77LOPdHMlSZKKlKcZS9IxsG7dOm6++WamTJkSG5UFmDFjBldddVXscfny5alWrVrs8SmnnELFihWZNWsW06dPJz4+nsaNG7Nw4UIaN24MwNatW+nWrRvjx4+PTQxVmKuvvppWrVpx3nnn0blzZwC++OILPvzwQwASEhJo3Lgx6enpx2TbJUmSioJhVpKO0q5du7jyyiu5++67c13P+t133/HVV1/RoEGDWFuLFi2YO3cumzdvJisri0mTJtGqVStmz57Nt99+S0ZGBmlpaVx00UWkpaUB0L17d6644gquvvrqQ67pscceY8SIEQRBAGRfy9utWze2bNnCpk2bSEtL4/zzzz9Ge0CSJOn4M8xK0lGaO3cuy5cvZ+TIkbH7ys6YMYN//OMfeSZrOvfccxk+fDgXX3wxCQkJVK1alT59+hTY94oVK5g9ezZTpkyJ9T127FgA4uPj2bVrV551Zs+eTenSpWnXrl2srWXLlvTq1Ys6depwySWXMHbs2EMa5ZUkSSquvGZWko5Sp06dYjMSHyi/0dRevXrRq1evAvurUaMG8+bNA7JnSi6o74yMjHzb27dvT/v27fO0Dxs2jGHDhhX4upIkSVHiyKwkSZIkKXIcmZWkY6zG0DeKuoSDWjOyQ1GXIEmSdFQcmZUkSZIkRY5hVpIkSZIUOYZZSZIkSVLkGGYlSZIkSZFjmJUkSZIkRY5hVpIkSZIUOYZZSZIkSVLkGGYlSZIkSZFjmJUkSZIkRY5hVpIkSZIUOYZZScfVyJEjqVChAhUrVmT06NEApKWl0alTJ9q2bcucOXMA2L17N3379iUhIYH69euzYMGCQvvYZ8uWLVSrVo3Vq1cfck0pKSnceOONhbYXVo8kSZKOv1JFXYCkX4733nuPqVOnsmzZMnbu3EmzZs3o0qULPXr0YOLEiQRBQNeuXUlPT2fKlCls376dNWvWsHz5cjp27MiqVav4+OOP8/TRtWtXEhMTAbjvvvvo3r07NWvWPKSaNm/ezN13383vfve7QtufeeaZfOspV67csd1JkiRJOiSGWUnHTZkyZXjhhRdiQbNGjRqsWbOGRx99lEsvvRSAxMRE1q9fz7Rp0xg5ciSlSpUiOTmZSpUqkZ6enm8fGzduJDExkWXLlpGamsrKlSsPuaZBgwZx2WWXHbS9oHqSk5OPZpdIkiTpCHmasaTjpmHDhpx33nkAbNy4kXXr1pGcnEzXrl3Zu3cvM2bMYM+ePdStWxeAzMxMALZt28batWupVKlSvn3Uq1cPgIEDB1K+fHmuv/56nn76acIwLLSeN998k/Xr13PrrbceUnt+9UiS9iVOrAAAHyFJREFUJKloODIrqUgMHz6cvn37UrZsWQCefPJJhg8fzhNPPEHJkiXp0aMHAwYMYOXKlbzyyivUrl2bGjVqFNjHwoULWbhwIY8//jiJiYkMHTqUIAjo169fvq+flZXF4MGDSU1NZcOGDQdtP5R6JEmSdPwYZiUdd3PmzOGdd95h+fLlsbbBgwfTtWtXmjZtStu2benXrx9xcXG8++67LF26lKlTpxbax+LFi+nYsSODBg0CYPv27bz00ksFhtmHHnqInj17UqtWrVyhtaD2g9UjSZKk48swK+m4WrduHTfffDOvvPIKZcuW5YsvvmDHjh00aNCAhIQEGjduTHp6OmeffTZXX301pUqV4q233qJz584F9gFQvnx5qlWrFlvmlFNOoWLFigXW8dprr5GZmcmYMWPYvXs3WVlZbN26lfT09HzbU1NTC6xHkiRJx5/XzEo6bnbt2sWVV17J3XffTZMmTYDs6167devGli1b2LRpE2lpaZx//vmxdR577DFGjBhBEAQF9gHQokUL5s6dy+bNm8nKymLSpEm0atWqwFo++eQTvvnmGzIyMkhNTaVbt26kpqYW2F5QPZIkSSoahllJx83cuXNZvnw5I0eOJD4+nvj4eDZv3kyvXr2oU6cOl1xyCWPHjqVKlSoAzJ49m9KlS9OuXbtC+5gxYwbnnnsuw4cP5+KLLyYhIYGqVavSp08fAOLj49m1a9dR159fPZIkSSoanmYs6bjp1KlTgTMMDxs2LE9b+/btad++/SH30atXL3r16pWnPSMjo9C6WrduTevWrQ/anl89kiRJKhqGWUlFrsbQN4q6hINaM7JDUZcgSZKk/XiasSRJkiQpcgyzkiRJkqTIMcxKkiRJkiLHMCtJkiRJihzDrCRJkiQpcgyzkiRJkqTIMcxKkiRJkiLHMCtJkiRJihzDrCRJkiQpcgyzkiRJkqTIMcxKkiRJkiLHMCtJkiRJihzDrCRJkiQpcgyzkiRJkqTIMcxKkiRJkiLHMCtJkiRJihzDrCRJkiQpcgyzkiRJkqTIMcxKkiRJkiLHMCtJkiRJipzDDrNBEFQIgmBOEAR/C4LglSAISgdB8N9BELwXBMF9+y13xG2SJEmSJBXmSEZmewBjwjC8HMgArgVKhmHYHKgZBEGtIAg6H2nbsdksSZIkSdKJrNThrhCG4fj9HlYGrgfG5jz+G9ACOB946QjbPj/wNYMguBW4FaBatWqHW7IkSZIk6QRzxNfMBkHQHDgdWAdsyGn+JxAHlD2KtjzCMHwmDMPkMAyTK1eufKQlS5IkSZJOEEcUZoMgOAMYB9wE/AicmvPUaTl9Hk2bJEmSJEmFOpIJoEoDLwPDwjBcCywh+/RggIbAmqNskyRJkiSpUId9zSzQB7gAGB4EwXAgBegZBEEVoB3QDAiBBUfYJkmSJElSoQ57ZDYMw6fDMDw9DMPWOf+eA1oDi4BLwzD8IQzDrUfadiw2SpIkSZJ0YjuSkdk8wjD8nn/PSnzUbZIkSZIkFcYJlyRJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5hllJkiRJUuQYZiVJkiRJkWOYlSRJkiRFjmFWkiRJkhQ5xSLMBkHw30EQvBcEwX1FXYskSZIkqfgr8jAbBEFnoGQYhs2BmkEQ1CrqmiRJkiRJxVuRh1mgNfBSzs9/A1oUXSmSJEmSpCgIwjAs2gKC4L+BP4dhuCIIgsuBC8IwHHnAMrcCt+Y8PBdIP85lFrVKQGZRF3EMnUjb47YUXyfS9rgtxdeJtD0n0rbAibU9bkvxdSJtj9tSfJ1o23MoqodhWPlgC5U6HpUcxI/AqTk/n0Y+o8VhGD4DPHM8iypOgiD4IAzD5KKu41g5kbbHbSm+TqTtcVuKrxNpe06kbYETa3vcluLrRNoet6X4OtG251gqDqcZL+HfpxY3BNYUXSmSJEmSpCgoDiOzM4EFQRBUAdoBzYq4HkmSJElSMVfkI7NhGG4lexKoRcClYRj+ULQVFUsn2inWJ9L2uC3F14m0PW5L8XUibc+JtC1wYm2P21J8nUjb47YUXyfa9hwzRT4BlCRJkiRJh6vIR2aVWxAE1wRBcMohLntIyxVnQRAERV2DVNSCICiZ33shyFbygLaT9/u59PGo71gLgqBEEAQnFXUdhyMIglJBEFQ4yDJBlP+mHc7voY6d/fd5EAQ1gyA48xDWKRUEQdmft7KDC4KgwMvVct7nR/R+OAHeS8X2+1kQBCdFbd8GQVA7CIKbcn4udTT17/+5GQRBxSAI6hyLGo+VA//e5ryPShywjPltP47MFiNBEJwDTAJahQf8xwRB8AdgcRiGb+3X9hrwCHAX0O3AdYpaEAT3A/8Mw/CpAp6/CBgO/O541R4EwZXAkjAMNxSyzMlAXBiGX+fzXGlgbxiGew9oD4CTwzDcmc86p4ZhmBUEQYkwDH86+q04ekEQXA98H4bhGwe0nx6G4fc5Py8CLgrD8Kec/ZYRhuH7RVBuoYIg6AecGobhfx7QXgVIBQ78PzkZuCMMww+OU4kHFQTBncCVwE/Ar4AyZE+GVwKYFYbh2Jzl/gDUCcOwZ87jt4CpYRhOKoq6CxIEwftAX+Bxsvf/qUAWUBoYBfwAjAd27LdaLbJvPVAnDMPVx7XgQxAEQTegaRiGgwtZ5g5gZxiGzx6/yg5dEAT/BD48oLkGcF0YhgsP9fewqOWEuFPIvlVfO+CPwAXADOALoC6QDCwHPiV7csn2ZH9e7q93GIbrjlPZ+cr5TPkb8GQYhq8EQfAS8D7ZtQOUBD4ARgP3hGH4bc561wK1wjA8cJuOqyAIXgQqAyFwEbBwv6dLAXeS/fvzKnlvq9iQ7G3Ykk+/Rf5eCoKgFTAC2FPAIiWBB8ie66VYfz/L+Y7yAnBTTk0tgIDs93or4Pf79nUQBPcAH4ZhOHe/9UsCC8IwvOh4157z+vFkv5cbAZOBCsC2nKeTgTPDMNwVBEGVMAw35qxzI7Bu//+XnPbROT8OA3oC1cIwfOBn34hCBEHwP2EY9gyC4FKgCfAbIJ7s371vgcfCMPxHEAQlwzDcGwTBu8BlYRjuCIKgRhiGa4qu+qJXHCaA+sULgmBezo+JwF7g7ZyDTiWAj8MwvB3YDewNguAysgMgZH9gjyb7C0dLYP5xLDuPnKPJrwO7cpqqAXuCILgm5/FPYRi2yVm2IvBnoE8YhmEQBP8JTAzDcOXPXGZj4EKyP4AKUgF4i+wv1wf6T6BhEAQ1yL7f1xay/5AuJfuPzm8g9sExOueL79ggCF4G6gZBsCsMw4n7OguC4HuyZ/TOT0ngpjAMvzrkrTt01cj+EDvQ/wRB8EQYhvOADUAd4BPgHrI/BItczkjAD0BaTlMF4IcgCDrlPK4NdArDcDHRmVAuBfgqDMNXgyDoAtQOw/DRnAC1Kuco7H1AfaBHEATtgfJAJ2BcEARtwzC8psDej5Oc/5sKwFagYhiGv805OLQwDMPfHLB4k5z/s1ZkB5O3gKfCMPzncS26EEEQ9AGGABv3a/s/sv82bw3D8KoDVtlDwV98i4M9wL8OaPuJf9dc6O/hcazzYCoB/0v2Z9+ZZH/+pQIvhmE4NAiCyWRvUxpwFfAK2dv9QRiGQwGCIJhEMfgOFIbh7iAIrgbuCoLgU7K3KYPsL7KQfQBoBdlBfXwQBNuBs8g5SJcTuBoCvw7D8MD/2+NR/7U5dZxNdiDvcOAyQRDUJ3ubFh3wVHWyv/Pkpzi8l94hey6X3TnvB8j+3VsZhuG8nL/LAdkHUor197Oc71mLgRvCMHwkCILywGvASmDIAQcNQnK+xwVB8HYYhpfmBKis4195TkFhmBEEQW9gw77vkfsEQbCc7O/IALcGQdAAGEj2wa61+XR3F9nfAROBAcDunPfRPo8cGICPgwpBEMwh++/aN2QffHuY7Elyl+Ts/wrAi2QfwPs70DsIgolkT6J7Xs4cRL9IRf6HXAD8luwvqaPI/uCtGYbhx5B91DYIgrZkf2ltDvwfMDkMw8lBEDxF9sjHT2QHj6L2T7L/8O/7kB0KZO4bNcr5UksQBKcD04GhYRiuyFn3v4GXgiC48lgeYQqCoAUwkuwP/pJkjxDtCoJgPtkflKWBKWSPEN0HbMpZNTPnIEMJsj+Meodh+FEYhv1z+h0LzMz5QFsehmHLA1660X4/1wH+AbwN/CXnCNy+Ean0nC/8p+w/qhsEwcVhGL57jHbDvqOqJ+/3uiXJOaqZ84Fchux9dBswNwiClsBioGVOMP8yDMPi8mU2JPsPfn+gKnAvsIzs37/lZB9Q+K7Iqjsye4HHgiD4bF9DzvvlQeAyoBzZI5vXkh0WxwLvhmH4ItkfaEnHveL8NeX/t3fuUXZX1R3/fMPkSZAYwZZXUXEBIvIoUNpCKCBUIiyKhcbII8ZFgAQJQeRdoKBCQxDqohowggKSgCDQtJLyKCEFC6VFSHizKIuHWEh5tE2kggOz+8d3/2Z+ubkzCTCZe4ecz1pZuY9z55577/mdc/be370PVEb1sZKewRu9JfVGku7Gn6eK5iwDHgXGpYJhn4Hrcp+8DVzcGPmWJYRz8/bfRsT0Zi+WdD5wZrsoMvD89q2GxybTsxdY1ThsCyLieUkXYiNuR+AGPH6mSNoAqy8i/+2K57JmkbGWq5kk7Qi8AZwF3IrXnMm1Jtfg3+XfgQXAscBJEfFK7W8cO1D97YMTsTOkGR3AxvQcw1gxFhhNz1rUVtdSKrB6M7ZJ1dJg2Z+BAwghaSb+3juxM7FT0rUR8aVsV1074LFJ7fEBR9JY4PyImNpbmzTWt4yIcySNw4bsMLy21P/W54DR2W4ScEdEnFJ7/nS83g40b+Fr/y3sqH4auA0HEeZiBcP/SqocR7Oxs/7zwIK12ZCFYsy2C6OAS4FDgY2AWXiAEhG/BW5Lz+YDWM7zTUlHAPfgxeAqVjSeWkJOJtMyEtsFbIonycl4rF0B/AC4BUvbTpd0YrbtAp4FFkgaFxH9YoxExM/JBTQX/FexhGtm5VHO5ybjyPDFef844KH3YVCejCO4C4Ft8Sak4lZJh1RysWSepF9gSdNOwA8k7d6PUaqPAXMlVZ77jwNvSDo17w8HjoqIxZJOwsbu9/HvMgf4637qx/smPZTPYsP7WGA7bNDugT3/E4FfZeSl+rxixYV4KHBGfzoM3g8R8WtZln8BaSRh58o1EfFi3r8wN+o34vG1XUagjl9D0fv3wi54ntoWb5zWx1KuR/JanxsRSyNiD0m74oj/cViBsF/Oc3/eor43ows4WZblg6NlL+ftauzs1cfr94+IM9ZU594N6ZS6kpXXisXAC7Da47BdmIGjK3dhh+UJWCVzAo6KVexF7+qXdqATuBj/NlPxZzoLXwe3ABPwvuBqelQC8yS9gyObzwEvRcTsge54haTtgSnAtrl2VizAn21xRGySbb8J3B0Rd+T9EcpUHNrwWpJTOw7GhtE7eK38P0lvAXdFxF8Ohv1ZGm5H4t9pK7wf+xXwSexEmZHtfgYsAg7I6C2SvoEN81YRWHG1Ko6RtAswLSIey2j68oY2S4BZuZZ+CDhPTtf5XCobRmBH60BTryHRifs5n569dMV3cSR9GnCzpNvxnLdWU4zZ9uAcvCjNxhfSNpJuxb/PPRFxbq3tMOBcPLk+hz0zT0ZDDmcLmQN8NyI6MzL7ZkR8J7371Xg7EvhvYGnESrnBu2Hpbr+RkpNx2EB7p/b4PsCLNWlz/TvcDOdn1P9OBzAkHQzN3qcDICfEY3C0dwKwYURcsopuTsSSpOuwvHlCf8otI+IZapLbnLxfiIiv1B7bQ87DaJR2bQVsJBfsmRHtkWvaiTd3U/P/ecAP8YJ7Bc7v3RO6ZXhHRsSk1nS1b9RTyOEmHGH+/bz/D8DifH4kdnZNB06IiIXA/NygPCHn2V0VEYtpLYvwZntyRNwj6Sj8mTqwXP3ynBd+jDdQ04HDyNzZiHgEeKQF/e6NDuAOHHEBG+iXA78GOtJD3pe0s+WRvxpX4ehYM/5YzsGEvseh2mitWZ+eKPPH8Nq5MCLOklTf/N2I19hZwERJO+fjn2LlKPWAkyqsA+TUm+9hg24THB36IjaQFuPI+Nt4D/A6vk6+HRGTW9DtbrLfc4EH6wolSTtg9cxE4C8k1a+Fz0j6at7uwM78W2jPa+m32ImwI3AfNkyfxE6tiQ1t23Z/FhFXS/oEvk46cWR2JDbOR0O3gmsEdv7+Gz0qmweAowe6zzVW+dtn30/G+aZV+4/gAEa1P1NEvAxMyr3mU8DeeN0aL8uwR9CTKjeQNFMcfJpaikuyADgjHSibAK9FxOMrvXItoxiz7cGpOPK1DC9cl0XEAZLWjxXP3R2HL7RFef8hvPGYOYB97RVJo7HxutKCFE7MH5JtNsSy2+e1YkG6zYAj1sDEfyTeSG/Eihvl4cBpWNI1DMuP78Tf8e8Bn5e0DBtGBwOTgOmSlgNbYgnuMuCTsiR5JM6pvS4ilufkehrwG7mAEsC6uODVCpHnNJCny4Vz1qNH7tzvSNoGS4fGStoqIp7KPtwN7JZttouIhyWNBMZGHwWzBpp0TmyKN+dP4nFzAc6NmRcRc2tthSMd0/P214DvtJHsE5xmcBo9i1lVeGfPvL8OWRgOj9OzJZ2dzw3HY3sZuSFpJZUxnVKo3fBm7yvA2RFxm6TDsPKgExtLP8LzwRBJt+FNyHo42twO0bQP46jlq8Bl2PCuNkrr0BoP/ntlG+CQXp77Kas/Duc2vrhFLMK/y+/ieWAsdgRPxQ44ACLi8ZyLx5I5tdCdM9sWpIE9EUsKL8MOuep3+Ax2/GyMjYtngP3wWvbZdHzPiYibWtDvLfHYOQvvYxp5JyKuAa6RdC9wfcPzX4qIXddwN/uLvXBUbHs8vkY2PN/W+7MagY3VKXh+Ww9fQxtip9CDwO8A/4hlr4djR0PLazKsgi/i6HIndK/92wN/n/vMdfB6Myfng4uwsusUvP+eCFTG/oAas5JG4d+konJwb0BDnnkqII/HDvx38Fyw1lOM2fbhBhyZvRe6iyncJBdI2R0nfN+MJ5aH8GSzFBtdt0naIqNvreTr2LtVGbObAl3qKZxQ5afeD8yPiCn1F8t5qP1a8EHShjif5XS8kH4fT2pExC2SviXnG64HPBdZpEbOKbk1XAiJbP9DHP1D0j3AFyLiVTlnds8mb38W3nj8MiJmpBE5tTcJtZwTtBB7dO+S9CeRlYX7i4yQfw/LUt7EE/v4Wi5tVR14oqT9sNH+E0nHR8Tt/dmX98ETWLp2SkRMT+P2pIiYJGk3SWdGRBVx+Roea49BdxR0Jl7A2oL8Xru/W9UK7zQ0nSvp/vpYkwuRzYwWV2LshT/EG4T6kS7HA50ZJRiB8wFfx46mF4FjI6IVXvHe2Ak4Dxc/GkVPNdwxOAp4ojRojrgYgmsyNGPUuxiHLUfSBByBWRdHX57HxuqlEfFPkuoqlClYothO46qRaTg6OQpH/R/BTtglwG8ytWIGlkvvgh14AHe2ODK7GV5L7qK5MVtnKR5/VZ5s5SSq067X0u7A5ak4W4gVJRsApKR4MOzP6nSEa3XsiaX5V2JH8DPAKZIW4Hoej0o6PAMM1+W11JZExDxs4AEg6cv4+/9ZRDzU0Pw4XKz0v/C19lGs4Ngb20UrnUqxhvkEHi+b5/sL7wUnYOdWY+R1RLZbB4/Dl1nLKcZsi5HL8t8A3BwR10v6NK76+x/pfbkV2CMifpz378QDexaePM/M1/8Ce5laRsqhuyXRdZlxvZ2c0/BnKUOqsxmu3NafLMfRlI2ATSPil2kAVByHJ64t8He9SuQjhTojonEhrrc5HudyHoxzaL6OC0SsVBFYPkJmDrCkFjUYhTcHp61On1az31vjifHKyMJbkmYDd0iaHBFPpyz0IGB8uCDVEknjsXfzyoi4qNc3GCByQ7EcywTBMrAqijGUHsnUAcCXgYvkfM2x+e/wdEDMoz0RvW/qQj3Vz8GL2nNrukPvgRHVWMnxLYCI+B9J68ny6On05GJvitUCD+dG6sLI4xVaRc7NW2PnyVexjPvcfO4IeiLhm9d+k42xA29y3t9iwDq8aobSuzHb7LzSvsZhq7kRRwR3AvbHcuF7sQwUbLhH/n93tt0SOLRm6G5FG0TNcj36eEQ8IB8/8in8+QCOwpVWl+CaAOfg6+YG3PeWnv0bWfE1nYTbNMxNo8lc5VzPHsXGw9NYqvsysLekHWrpEe16LR0C7JPrSMUoLPm8fjDszzIauSM2mB7Mh1/A0b0/An6Uzu7N8ZGKVWChXeeAOt22TI7Fabiw4NG4/kgXVvu8LmkrnJ60Y+5xjs7XjcSKuNkMfPrBvljWPR8b1l14fN2H98U7y6eFbJ793RrPe2OAayU9iOvR3NtmqrMBoxizrWcILopyvaRtceTyPIBwpdyDImJptn0Ce5470+idkknuj+Vz7cYomo+xofQemR3WpP17JiLelPSf2Eg+Sc77HElKBSMLAEnaCXhWkprk8Q7Jtl05EV7BikZph/Lsr2y/Ky6qMAF/B6/hUvD34Mmnserca8DfRK0UfERcnAtLv5DyzvOBY6J2dlxE/ETSm8CduUH/U+CgqFVWjohn04N7jqRR9ShuCwnoPiaF2u0P4ygB+Hu9GktB78eez6X46IR2lkwNz3/N6GgSmf32mu/Su2ZU7XY9Xx48520HHBgRL8nHvgyLiEsk/R3wBdrD07w/lgmehAtaTcko7E3YEDoAICLG9PYHJD0qqaNZ6kULWN6LggRJjZEL6HsctpTaXLshHlt74yjlG3Khv52wI3N4ZBV2Sa9gZcyCvD8e125oNZuQRnXm8+0D3RLea7EDYmdcD+CvgEsi4rp01k1NQ3cYPh1gfgv6DzaqH2+Ym3bAhjfYyf0vOAp2PT4z86epcDpG0sMR0dWm11IHrlOwqKE/W+NjbqqIf7vvz6bj3OufY+XV7fj6rhzAH8FO/8/iOa/iJeg2huvz+kAyotmDOR/fjwsMjsDS6UNxOsFh2exQOc3rnyXtGxFPSdqrvsdJurDT8qIY+GJ3j+Njw14DkNWCxwGnRsTbkmbRc9TTBVGr6iynKByO6zkcgdOO1joUrT3HubAWkpNix0DICdMj/K94ApgrH4NwL54kLsk2BwIHYnnXH7ByIv5Q7Km7HRdDuTQibq69x1PADuFqjNVjHXjTOwQXdboWG4rTsPRl34h4RX2fMyucP33D+/gKqv4MBUZGL+XbJa0bEW80e65QWFtJR9ZH08ioHivXShtSOSIlDYteivQNJnKjPqy+Ttadpu2GVq7x8YEg186udv3eV5fck3StKnLXRg7r1aIegEgH1X0RsVIRUUljmj1e+GBQjNnCB55m0daG58cAH4qIF/r5fUfWDdza40NWRwqSmxmtrbKRQqFQKBQKhUKhL4oxWygUCoVCoVAoFAqFQceQVTcpFAqFQqFQKBQKhUKhvSjGbKFQKBQKhUKhUCgUBh3FmC0UCoVCoVAoFAqFwqCjGLOFQqFQKBQKhUKhUBh0FGO2UCgUCoVCoVAoFAqDjv8HZaAnmbHaOTsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,12))\n",
    "plt.bar(xlist, ylist)\n",
    "for a, b in zip(xlist, ylist):\n",
    "    plt.text(a, b + 0.02, b, ha = 'center', va = 'bottom', fontsize = 12)\n",
    "plt.title('各区单价统计情况')#绘制标题\n",
    "plt.savefig('./3.2各区单价统计情况柱状图.jpg')#保存图片\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
